Search

ObjectiveClinical guidelines recommend annual eye examinations to detect diabetic retinopathy (DR) in patients with diabetes. However, timely DR detection remains a problem in medically underserved and under-resourced settings in the United States. Machine learning that identifies patients with latent/undiagnosed DR could help to address this problem.Materials and methodsUsing electronic health record data from 40 631 unique diabetic patients seen at Los Angeles County Department of Health Services healthcare facilities between January 1, 2015 and December 31, 2017, we compared ten machine learning environments, including five classifier models, for assessing the presence or absence of DR. We also used data from a distinct set of 9300 diabetic patients seen between January 1, 2018 and December 31, 2018 as an external validation set.ResultsFollowing feature subset selection, the classifier with the best AUC on the external validation set was a deep neural network using majority class undersampling, with an AUC of 0.8, the sensitivity of 72.17%, and specificity of 74.2%.DiscussionA deep neural network produced the best AUCs and sensitivity results on the test set and external validation set. Models are intended to be used to screen guideline noncompliant diabetic patients in an urban safety-net setting.ConclusionMachine learning on diabetic patients' routinely collected clinical data could help clinicians in safety-net settings to identify and target unscreened diabetic patients who potentially have undiagnosed DR.

The early detection and treatment of DR have helped the ophthalmologist to treat the affected patients and to reduce vision loss. Computer-aided screening for automatic DR detection in the medical system has consistent detection of lesions in retinal fundus image. To overcome these challenges and to offer timely treatment, this paper aims to develop a novel deep learning-based DR detection. Here, the integration of 'Optimized Iterative Thresholding (O-IT)' is adopted for the accurate segmentation of blood vessels. The first novelty of this work is that the thresholding approach is improved by tuning the parameters in the proposed model by developing a hybrid meta-heuristic Shark Smell-Jaya Optimisation (SS-JO) algorithm to enhance the performance of both blood vessel segmentation and classification. CNN is replaced by a deep learning framework termed as optimised 'Long Short-Term Memory (LSTM)'. The second novelty of this work is that the optimised LSTM is designed in the proposed model by optimising the parameters in LSTM using the implemented SS-JO to reduce the complexity of the network. The accuracy analysis of the implemented SS-JO-CN-LSTM is secured 44%, 29%, 19%, 6%, 4% and 15% improved than SVM, NN, CNN, LSTM, CN-LSTM and FR-CSA-NN+CNN. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) is a vision disease due to the long-term prevalence of Diabetes Mellitus. It affects the retina of the eye and causes severe damage to the vision. If not treated on time it may lead to permanent vision loss in diabetic patients. Today's development in science has no medication to cure Diabetic Retinopathy. However, if diagnosed at an early stage it can be controlled and permanent vision loss can be avoided. Compared to the diabetic population, experts to diagnose Diabetic Retinopathy are very less in particular to local areas. Hence an automatic computer-aided diagnosis for DR detection is necessary. In this paper, we propose an unsupervised clustering technique to automatically cluster the DR into one of its five development stages. The deep learning based unsupervised clustering is made to improve itself with the help of fuzzy rough c-means clustering where cluster centers are updated by fuzzy rough c-means clustering algorithm during the forward pass and the deep learning model representations are updated by Stochastic Gradient Descent during the backward pass of training. The proposed method was implemented using python and the results were taken on DGX server with Tesla V100 GPU cards. An experimental result on the publically available Kaggle dataset shows an overall accuracy of 88.7%. The proposed model improves the accuracy of DR diagnosis compared to the existing unsupervised algorithms like k-means, FCM, auto-encoder, and FRCM with alexnet. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) is a sight-threatening condition caused by diabetes. Screening programmes for DR include eye examinations, where the patient's fundi are photographed, and the findings, including DR severity, are recorded in the medical report. However, statistical analyses based on DR severity require structured labels that calls for laborious manual annotation process if the report format is unstructured. In this work, we propose a large language model DR-GPT for classification of the DR severity from unstructured medical reports. On a clinical set of medical reports, DR-GPT reaches 0.975 quadratic weighted Cohen's kappa using truncated Early Treatment Diabetic Retinopathy Study scale. When DR-GPT annotations for unlabeled data are paired with corresponding fundus images, the additional data improves image classifier performance with statistical significance. Our analysis shows that large language models can be applied for unstructured medical report databases to classify diabetic retinopathy with a variety of applications., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Jaskari et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Delayed diagnosis and treatment of vision-threatening diabetic retinopathy (VTDR) is a common cause of visual impairment in individuals with type 2 diabetes mellitus (T2DM). Identification of VTDR predictors is the key to early prevention and intervention, but the predictors from previous studies are inconsistent. This study aims to conduct a systematic review and meta-analysis of the existing evidence for VTDR predictors, then to develop a risk prediction model after quantitatively summarising the predictors across studies, and finally to validate the model with two Chinese cohorts., Methods: We systematically retrieved cohort studies that reported predictors of VTDR in T2DM patients from PubMed, Ovid, Embase, Scopus, Cochrane Library, Web of Science, and ProQuest from their inception to December 2023. We extracted predictors reported in two or more studies and combined their corresponding relative risk (RRs) using meta-analysis to obtain pooled RRs. We only selected predictors with statistically significant pooled RRs to develop the prediction model. We also prospectively collected two Chinese cohorts of T2DM patients as the validation set and assessed the discrimination and calibration performance of the prediction model by the time-dependent ROC curve and calibration curve., Results: Twenty-one cohort studies involving 622 490 patients with T2DM and 57 107 patients with VTDR were included in the meta-analysis. Age of diabetes onset, duration of diabetes, glycosylated haemoglobin (HbA1c), estimated glomerular filtration rate (eGFR), hypertension, high albuminuria and diabetic treatment were used to construct the prediction model. We validated the model externally in a prospective multicentre cohort of 555 patients with a median follow-up of 52 months (interquartile range = 39-77). The area under the curve (AUC) of the prediction model was all above 0.8 for 3- to 10-year follow-up periods and different cut-off value of each year provided the optimal balance between sensitivity and specificity. The data points of the calibration curves for each year closely surround the corresponding dashed line., Conclusions: The risk prediction model of VTDR has high discrimination and calibration performance based on validation cohorts. Given its demonstrated effectiveness, there is significant potential to expand the utilisation of this model within clinical settings to enhance the detection and management of individuals at high risk of VTDR., Competing Interests: Disclosure of interest: The authors completed the ICMJE Disclosure of Interest Form (available upon request from the corresponding author) and disclose no relevant interests., (Copyright © 2024 by the Journal of Global Health. All rights reserved.)

Background: Diabetic retinopathy (DR), a chronic and progressive microvascular complication of diabetes mellitus, substantially threatens vision and is a leading cause of blindness among working-age individuals worldwide. Traditional diagnostic methods, such as ophthalmoscopy and fluorescein angiography are nonquantitative, invasive, and time consuming. Analysis of protein biomarkers in tear fluid offers noninvasive insights into ocular and systemic health, aiding in early DR detection. This study introduces a surface acoustic wave (SAW) microchip that rapidly enhances fluorescence in bead-based immunoassays for the sensitive and noninvasive DR detection from human tear samples., Results: The device facilitated particle mixing for immunoassay formation and particle concentration in the droplet, resulting in an enhanced immunofluorescence signal. This detachable SAW microchip allows the disposal of the cover glass after every use, thereby improving the reusability of the interdigital transducer and minimizing potential cross-contamination. A preliminary clinical test was conducted on a cohort of 10 volunteers, including DR patients and healthy individuals. The results demonstrated strong agreement with ELISA studies, validating the high accuracy rate of the SAW microchip., Significance: This comprehensive study offers significant insights into the potential application of a novel SAW microchip for the early detection of DR in individuals with diabetes. By utilizing protein biomarkers found in tear fluid, the device facilitates noninvasive, rapid, and sensitive detection, potentially revolutionizing DR diagnostics and improving patient outcomes through timely intervention and management of this vision-threatening condition., Competing Interests: Declaration of competing interest Authors hereby declare that this article is original, unpublished, and is not being considered for publication elsewhere. We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship. We further confirm that the order of authors listed in the manuscript has been approved by all of us. We have also duly acknowledged all the funding agencies. Thus, the authors declare no conflict of interest. The corresponding author will be the sole contact for the Editorial process (including Editorial Manager and direct communications with the office). He is responsible for communicating with the other authors about progress, submissions of revisions, and final approval of proofs., (Copyright © 2024 Elsevier B.V. All rights reserved.)

To evaluate the intra- and interdevice repeatability of microperimetry (MP) assessments in patients with diabetic macular edema (DME) two consecutive MP testings (45 fovea-centered stimuli, 4-2 staircase strategy) were performed using MP3 (NIDEK, Aichi, Japan) and MAIA (CenterVue, Padova, Italy), respectively. Intraretinal fluid (IRF) and ellipsoid zone (EZ) thickness were automatically segmented by published deep learning algorithms. Hard exudates (HEs) were annotated semi-automatically and disorganization of retinal inner layers (DRIL) was segmented manually. Point-to-point registration of MP stimuli to corresponding spectral-domain OCT (Spectralis, Heidelberg Engineering, Germany) locations was performed for both devices. Repeatability was assessed overall and in areas of disease-specific OCT biomarkers using Bland-Altmann coefficients of repeatability (CoR). A total of 3600 microperimetry stimuli were tested in 20 eyes with DME. Global CoR was high using both devices (MP3: ± 6.55 dB, MAIA: ± 7.69 dB). Higher retest variances were observed in stimuli with IRF (MP3: CoR ± 7.4 dB vs. ± 6.0 dB, p = 0.001, MAIA: CoR ± 9.2dB vs. ± 6.8 dB, p = 0.002) and DRIL on MP3 (CoR ± 6.9 dB vs. ± 3.2 dB, p < 0.001) compared to stimuli without. Repeatabilities were reduced in areas with thinner EZ layers (both p < 0.05). Fixation (Fuji classification) was relatively unstable independent of device and run. These findings emphasize taking higher caution using MP in patients with DME., (© 2024. The Author(s).)

Purpose: To investigate the association between mean ocular perfusion pressure (MOPP), estimated cerebrospinal fluid pressure (CSFP), and changes in diabetic retinopathy (DR) in a Southern Chinese population with type 2 diabetes (T2DM)., Methods: A total of 1224 subjects from the Guangzhou Diabetic Eye Study were enrolled. Systolic blood pressure (SBP), diastolic blood pressure (DBP), and intraocular pressure (IOP) were measured. MOPP was calculated with the formula: MOPP = 2/3 [DBP + 1/3 (SBP - DBP)] - IOP. CSFP was calculated using the formula: CSFP = 0.44 × body mass index (kg/m2) + 0.16 × DBP - 0.18 × age (years) - 1.91. Incidence, progression, and regression of DR were graded based on seven-field 45° conventional fundus photographs at baseline and during two-year follow-up examinations according to the United Kingdom National Diabetic Eye Screening Program guidelines., Results: Higher MOPP was associated with DR incidence in the multivariate model (per 1 mm Hg increase: relative risk, 1.05; 95% confidence interval, 1.01-1.09; P = 0.02) and was not associated with DR development and DR regression in two-year follow-up of T2DM patients. However, CSFP was not associated with DR changes (incidence, progression, or regression)., Conclusions: The higher MOPP is an independent risk factor for DR incidence among T2DM patients in a Southern Chinese cohort. Monitoring MOPP and managing blood pressure can be part of a comprehensive approach to prevent or delay the onset of DR in T2DM patients., Translational Relevance: MOPP might be an indicator for the detection of DR incidence.

Purpose: To identify a reliable biomarker for screening diabetic nephropathy (DN) using artificial intelligence (AI)-assisted ultra-widefield swept-source optical coherence tomography angiography (UWF SS-OCTA)., Methods: This study analyzed data from 169 patients (287 eyes) with type 2 diabetes mellitus (T2DM), resulting in 15,211 individual data points. These data points included basic demographic information, clinical data, and retinal and choroidal data obtained through UWF SS-OCTA for each eye. Statistical analysis, 10-fold cross-validation, and the random forest approach were employed for data processing., Results: The degree of retinal microvascular damage in the diabetic retinopathy (DR) with the DN group was significantly greater than in the DR without DN group, as measured by SS-OCTA parameters. There were strong associations between perfusion density (PD) and DN diagnosis in both the T2DM population (r = -0.562 to -0.481, P < 0.001) and the DR population (r = -0.397 to -0.357, P < 0.001). The random forest model showed an average classification accuracy of 85.8442% for identifying DN patients based on perfusion density in the T2DM population and 82.5739% in the DR population., Conclusions: Quantitative analysis of microvasculature reveals a correlation between DR and DN. UWF PD may serve as a significant and noninvasive biomarker for evaluating DN in patients through deep learning. AI-assisted SS-OCTA could be a rapid and reliable tool for screening DN., Translational Relevance: We aim to study the pathological processes of DR and DN and determine the correspondence between their clinical and pathological manifestations to further clarify the potential of screening DN using AI-assisted UWF PD.

Purpose: The purpose of this study was to investigate whether the rapid rate of peripapillary retinal nerve fiber layer (pRNFL) thinning in short-term is associated with the future risk of developing diabetic retinopathy (DR)., Methods: This prospective cohort study utilized 4-year follow-up data from the Guangzhou Diabetic Eye Study. The pRNFL thickness was measured by optical coherence tomography (OCT). DR was graded by seven-field fundus photography after dilation of the pupil. Correlations between pRNFL thinning rate and DR were analyzed using logistic regression. The additive predictive value of the prediction model was assessed using the C-index, net reclassification index (NRI), and integrated discriminant improvement index (IDI)., Results: A total of 1012 patients with diabetes (1012 eyes) without DR at both baseline and 1-year follow-up were included in this study. Over the 4-year follow-up, 132 eyes (13%) developed DR. After adjusting for confounding factors, a faster rate of initial pRNFL thinning was significantly associated with the risk of DR (odds ratio per standard deviation [SD] decrease = 1.15, 95% confidence interval [CI] = 1.08 to 1.23, P < 0.001). Incorporating either the baseline pRNFL thickness or its thinning rate into conventional prediction models significantly improved the discriminatory power. Adding the rate of pRNFL thinning further enhanced the discriminative power compared with models with only baseline pRNFL thickness (C-index increased from 0.685 to 0.731, P = 0.040). The IDI and NRI were 0.114 and 0.463, respectively (P < 0.001)., Conclusions: The rate of initial pRNFL thinning was associated with DR occurrence and improved discriminatory power of traditional predictive models. This provides new insights into the management and screening of DR.

Background: Optical coherence tomography angiography (OCTA) is a relatively new extension of Optical coherence tomography (OCT) that generates non-invasive, depth-resolved images of the retinal microvasculature which allows for the detection of various features of diabetic retinopathy., Objectives: This study aimed to detect biomarkers that may predict an early anatomical response to the treatment of diabetic macular edema (DME) with intravitreal ranibizumab (IVR) by means of OCTA., Patients and Methods: This prospective interventional study was undertaken on 111 eyes of 102 naïve participants who had diabetic macular edema; enrolled patients were evaluated by taking a complete ophthalmologic history, examination and investigations by use of a pre-designed checklist involving Optical Coherence Tomography Angiography., Results: Regarding the best corrected visual acuity (BCVA) the Mean ± SD was 0.704 ± 0.158 preoperatively and 0.305 ± 0.131 postoperatively in good responder patients; and was 0.661 ± 0.164 preoperatively and 0.54 ± 0.178 postoperatively in poor responders. The central macular thickness (CMT) was 436.22 ± 54.66 μm preoperatively and 308.12 ± 33.09 μm postoperatively in good responder patients; and was 387.74 ± 44.05 μm preoperatively and 372.09 ± 52.86 μm postoperatively in poor responders. By comparing the pre injection size of the foveal avascular zone area (FAZ-A) in both groups, it found that the mean ± SD of FAZ-A was 0.297 ± 0.038 mm in good responder patients compared to 0.407 ± 0.05 mm in non-responder patients. The preoperative superficial capillary plexus (SCP) foveal vascular density (VD) was 24.02 ± 3.01% in good responder patients versus 17.89 ± 3.19% um in poor responders. The preoperative SCP parafoveal VD was 43.06 ± 2.67% in good responder patients versus 37.96 ± 1.82% um in poor responders. The preoperative deep capillary plexus (DCP) foveal VD was 30.58 ± 2.89% in good responder patients versus 25.45 ± 3.14% in poor responders. The preoperative DCP parafoveal VD was 45.66 ± 2.21% in good responder patients versus 43.26 ± 2.35% um in poor responders, this was statistically significant., Conclusion: OCTA offers an accurate measurement for VD in the macula as well as the FAZ-A which could be used to predict an early anatomical response of anti-VEGF treatment in DME., (© 2024. The Author(s).)

Importance: Biosimilars may be lower-cost alternatives to originator biologic products, potentially offering expanded access or reduced economic burden, but have not been evaluated with aflibercept in diabetic macular edema (DME)., Objective: To compare efficacy and safety of MYL-1701P, an aflibercept biosimilar, with reference aflibercept (Eylea [Regeneron]) in DME., Design, Setting, and Participants: This was a double-masked, randomized clinical trial that included participants at 77 centers across the US, Europe, Japan, and India. Included in the analysis were individuals 18 years and older with type 1 or type 2 diabetes with central DME and best-corrected visual acuity (BCVA) letter score of 73 to 38 in the study eye using an Early Treatment Diabetic Retinopathy Study (ETDRS) chart. Study data were analyzed from October to December 2021., Interventions: Formulations of MYL-1701P (0.5-mg vial) or reference aflibercept every 4 weeks for 5 consecutive intravitreal injections, followed by every 8 weeks through week 52., Main Outcomes and Measures: The primary outcome was the adjusted difference in least squares mean (SE) change from baseline BCVA letter score at week 8 with an equivalence margin of -3 to +3 letters. Secondary outcomes included change in central subfield thickness (CST), BCVA, number of injections over 52 weeks, incidence of adverse events (AEs), and antidrug antibodies (ADAs)., Results: A total of 355 participants (mean [SD] age, 62.2 [9.2] years; 216 male [60.8%]) were randomized to MYL-1701P (179 participants [50.4%]) and aflibercept (176 participants [49.6%]). At week 8, mean (SE) change in BCVA was 6.60 (0.55) letters vs 6.56 (0.55) letters in the MYL-1701P vs aflibercept groups. The adjusted mean difference of 0.04 letters (90% CI, -1.16 to 1.24 letters) met the primary outcome. At week 8, mean (SE) change in CST was -112 (7) μm vs -124 (7) μm in the MYL-1701P vs aflibercept groups (adjusted mean difference, 12 μm; 90% CI, -3 to 26 μm). The incidence of treatment-emergent AEs in the MYL-1701P and aflibercept arms were ocular (30.9% [55 of 178] vs 29.5% [52 of 176]), serious ocular (0.6% [1 of 178] vs 1.1% [2 of 176]), nonocular (65.2% [116 of 178] vs 65.3% [115 of 176]), and serious nonocular (16.9% [30 of 178] vs 11.9% [21 of 176]). The mean (SD) total number of injections was 8.4 (2.1) vs 8.7 (1.8) in the MYL-1701P vs aflibercept groups. The incidence of treatment-induced or treatment-boosted ADAs was 2.8% (5 of 177) vs 5.7% (10 of 176) in the MYL-1701P vs aflibercept arms., Conclusions and Relevance: MYL-1701P demonstrated clinical equivalence in regard to efficacy, with comparable safety and immunogenicity, to reference aflibercept. These findings support use of MLY-1701P as an alternative to reference aflibercept., Trial Registration: ClinicalTrials.gov Identifier: NCT03610646.

Importance: Besides race, little is known about how other social determinants of health (SDOH) affect quality of diabetic eye care., Objective: To evaluate the association between multiple SDOH and monitoring for diabetic retinopathy (DR) in accordance with clinical practice guidelines (CPGs)., Design, Setting, and Participants: This cohort study was conducted in 11 US medical centers and included adult patients (18-75 years old) with diabetes. Patients received care from 2012 to 2023 and had 18 months or more of follow-up., Exposures: Multiple SDOH and associated factors, including ethnicity, urbanicity of residence, health insurance type, and diabetes type., Main Outcomes and Measures: Adjusted odds ratio (aOR) of receiving 1 or more eye-care visits and 1 or more dilated fundus examinations in accordance with CPGs., Results: The study cohort included 37 397 adults with diabetes: 10 157 Black patients and 27 240 White patients. The mean (SD) age was 58 (11) years for Black patients and 59 (11) years for White patients. Of the Black patients, 6422 (63.2%) were female and 3735 (36.8%) male; of the White patients, 13 120 (48.1) were female and 14 120 (51.8) were male. Compared with those of the same race in urban communities, Black patients (aOR, 0.12; 95% CI, 0.04-0.31) and White patients (aOR, 0.75; 95% CI, 0.62-0.91) with diabetes living in rural communities had 88% and 25% lower odds of having eye-care visits, respectively. Sicker Black and White patients, defined by the Charlson Comorbidity Index, had 4% (aOR, 1.04; 95% CI, 1.02-1.06) and 5% (aOR, 1.05, CI 1.04-1.06) higher odds of having an eye-care visit, respectively. Black patients with preexisting DR had 15% lower odds of visits (aOR, 0.85, CI 0.73-0.99) compared with those without preexisting DR while White patients with preexisting DR had 16% higher odds of eye-care visits (aOR, 1.16; 95% CI, 1.05-1.28). White patients with Medicare (aOR, 0.85; 95% CI, 0.80-0.91) and Medicaid (aOR, 0.81; 95% CI, 0.68-0.96) had lower odds of eye-care visits vs patients with commercial health insurance. Hispanic White patients had 15% lower odds of eye-care visits (aOR, 0.85; 95% CI, 0.74-0.98) vs non-Hispanic White patients. White patients with type 1 diabetes had 17% lower odds of eye-care visits (aOR, 0.83; 95% CI, 0.76-0.90) vs those with type 2 diabetes. Among patients who had eye-care visits, those with preexisting DR (Black: aOR, 1.68; 95% CI, 1.11-2.53; White: aOR, 1.51; 95% CI, 1.16-1.96) were more likely to undergo dilated fundus examinations., Conclusions and Relevance: This study found that certain SDOH affected monitoring for DR similarly for Black and White patients with diabetes while others affected them differently. Patients living in rural communities, Black patients with preexisting DR, and Hispanic White patients were not receiving eye care in accordance with CPGs, which may contribute to worse outcomes.

Objective: To assess the feasibility of using non-mydriatic fundus photography in conjunction with an artificial intelligence (AI) reading platform for large-scale screening of diabetic retinopathy (DR)., Methods: In this study, we selected 120 patients with diabetes hospitalized in our institution from December 2019 to April 2021. Retinal imaging of 240 eyes was obtained using non-mydriatic fundus photography. The fundus images of these patients were divided into two groups based on different interpretation methods. In Experiment Group 1, the images were analyzed and graded for DR diagnosis using an AI reading platform. In Experiment Group 2, the images were analyzed and graded for DR diagnosis by an associate chief physician in ophthalmology, specializing in fundus diseases. Concurrently, all patients underwent the gold standard for DR diagnosis and grading-fundus fluorescein angiography (FFA)-with the outcomes serving as the Control Group. The diagnostic value of the two methods was assessed by comparing the results of Experiment Groups 1 and 2 with those of the Control Group., Results: Keeping the control group (FFA results) as the gold standard, no significant differences were observed between the two experimental groups regarding diagnostic sensitivity, specificity, false positive rate, false negative rate, positive predictive value, negative predictive value, Youden's index, Kappa value, and diagnostic accuracy (X 2 = 0.371, P > 0.05)., Conclusion: Compared with the manual reading group, the AI reading group revealed no significant differences across all diagnostic indicators, exhibiting high sensitivity and specificity, as well as a relatively high positive predictive value. Additionally, it demonstrated a high level of diagnostic consistency with the gold standard. This technology holds potential for suitability in large-scale screening of DR., Competing Interests: Declaration of competing interest All authors have contributed significantly to the manuscript and declare that the work is original and has not been submitted or published elsewhere. None of the authors have any financial disclosure or conflict of interest., (Copyright © 2024. Published by Elsevier B.V.)

Aims: Diabetic nephropathy, vision loss and diabetic retinopathy (DR) are frequent comorbidities among individuals with type 2 diabetes (T2D). The Retinopathy in People Currently On Renal Dialysis (RiPCORD) study sought to examine the epidemiology and risk of vision impairment (VI) and DR among a cohort of Indigenous and non-Indigenous Australians with T2D currently receiving haemodialysis for end-stage renal failure (ESRF)., Methods: A total of 106 Indigenous and 109 non-Indigenous Australians were recruited in RiPCORD across five haemodialysis centres in urban and remote settings. Clinical assessments, questionnaires and medical record data determined the rates of ocular complications and risk factor profiles., Results: Prevalence rates include unilateral VI, 23.5 %; bilateral VI, 11.7 %; unilateral blindness, 14.2 %; and bilateral blindness, 3.7 %, with no significant differences between sub-cohorts (p=0.30). DR prevalence rates were 78.0 % among non-Indigenous Australians and 93.1 % among Indigenous Australians (p=<0.001). Non-Indigenous ethnicity (OR: 0.28) and pre-dialysis diastolic blood pressure (OR: 0.84 per 10-mmHg) were protective, while peripheral vascular disease (OR: 2.79) increased DR risk., Conclusions: Ocular complications among individuals with T2D and ESRF are disproportionately high, especially for Indigenous Australians, and beyond what can be accounted for by risk factor variation. Findings suggest a need to improve screening and preventative efforts within this high-risk population group., Competing Interests: Declaration of competing interest The authors have no conflict of interest to declare., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Importance: While combined treatment of anti-vascular endothelial growth factor (VEGF) injections plus panretinal photocoagulation (PRP) is a common approach for treating proliferative diabetic retinopathy (PDR) in the clinical practice setting, large randomized clinical trials typically use monotherapy. Subsequently, information is limited as to whether the order of treatment when combining PRP and anti-VEGF injections for PDR affects outcomes., Objective: To compare outcomes of patients with PDR treated with PRP and subsequent anti-VEGF injections with outcomes of matched patients treated with anti-VEGF injections and subsequent PRP., Design, Setting, and Participants: This retrospective cohort study used data from January 2003 to January 2024 in the TriNetX aggregated electronic health records network, with data analysis performed in January 2024. Patients with PDR treated with PRP and anti-VEGF injections were eligible for inclusion., Exposures: Patients with new PDR diagnoses stratified by therapy with PRP and subsequent anti-VEGF injections or anti-VEGF injections and subsequent PRP., Main Outcomes and Measures: The primary outcome was the need for pars plana vitrectomy (PPV), defined by Current Procedural Terminology codes 67040 or 67113. The secondary outcome included incidence of PPV, vitreous hemorrhage (VH), or tractional retinal detachment (TRD). Relative risk ratios, relative risk differences, and 95% CIs were all calculated for univariate comparison of the cohorts and the development of primary outcomes after matching., Results: Before propensity score matching (PSM), which controlled for baseline demographic characteristics and medical comorbidities, 2167 patients with PDR treated with PRP first and subsequent anti-VEGF injections and 1549 patients with PDR treated with anti-VEGF injections and subsequent PRP were included. Post-PSM, mean (SD) participant age was 63.0 (13.1) years in cohort 1 (PRP and subsequent anti-VEGF injection) and 63.0 (12.4) years in cohort 2 (anti-VEGF injection and subsequent PRP). Of 1377 total participants in each cohort, 641 patients (46.6%) and 663 patients (48.1%) in cohorts 1 and 2 were female, respectively. Treatment with PRP first and subsequent anti-VEGF injection was associated with higher rates of PPV at 5 years compared with patients treated with anti-VEGF injection and subsequent PRP (relative risk [RR], 1.88; 95% CI, 1.55-2.27; risk difference [RD], 8.93%; 95% CI, 6.31%-11.55%; P < .001), with similar associations at 6 months, 1 year, and 3 years. Treatment with PRP and subsequent anti-VEGF injection was associated with higher rates of VH at 5 years (RR, 1.40; 95% CI, 1.09-1.80; RD, 6.47%; 95% CI, 1.66%-11.29%; P < .001) and TRD at 5 years (RR, 1.85; 95% CI, 1.35-2.53; RD, 4.31%; 95% CI, 2.10%-6.52%; P < .001), with similar findings at 6 months, 1 year, and 3 years compared with patients treated with anti-VEGF injection and subsequent PRP., Conclusions and Relevance: In this retrospective cohort study, findings suggest that patients with PDR treated with PRP first then subsequent anti-VEGF injection are more likely to undergo PPV for VH and TRD compared with matched patients treated with anti-VEGF agents first, then PRP. These findings support the need for further investigations to determine if the order of PRP and anti-VEGF injections should be considered when treating patients with PDR.

Background/objectives: Anti-VEGF treatment response in DMO has been measured by changes in the central subfield thickness (CST) and best visual acuity (BVA) outcomes at 3 months after initial treatment, termed early or limited early response (ER/LER). This study correlates LER with 12-month BVA, CST, and retinal fluid volumes quantified by a machine learning algorithm on optical coherence tomography (OCT)., Subjects/methods: The study included treatment naïve DMO patients ≥ 18 years with OCT scans at baseline (M0), M3, M6, and M12. The 220 patients were categorized as limited early responders (LER) if they had ≤ 10% CST reduction and/or < 5 ETDRS letter gain at M3. BVA, CST, and subretinal (SRF), intraretinal (IRF), and total retinal (TRF) fluid volumes quantified by a machine learning algorithm were compared between groups and across time., Results: At M12, the anatomic LER (aLER), defined solely by CST, had significantly worse BVA and CST versus the anatomic ER (aER) group (p < 0.001). Retinal fluid M12 outcomes did not significantly vary between all LER and ER groups. No significant BVA, CST, TRF, and IRF variance across time for LER was found (p > 0.1)., Conclusions: BVA and CST M12 outcomes vary by aLER/aER status indicating that CST may be a strong predictor of treatment outcomes, while retinal fluid volumes were not predicted by LER status., (© 2024. The Author(s).)

Primary diabetes care and diabetic retinopathy (DR) screening persist as major public health challenges due to a shortage of trained primary care physicians (PCPs), particularly in low-resource settings. Here, to bridge the gaps, we developed an integrated image-language system (DeepDR-LLM), combining a large language model (LLM module) and image-based deep learning (DeepDR-Transformer), to provide individualized diabetes management recommendations to PCPs. In a retrospective evaluation, the LLM module demonstrated comparable performance to PCPs and endocrinology residents when tested in English and outperformed PCPs and had comparable performance to endocrinology residents in Chinese. For identifying referable DR, the average PCP's accuracy was 81.0% unassisted and 92.3% assisted by DeepDR-Transformer. Furthermore, we performed a single-center real-world prospective study, deploying DeepDR-LLM. We compared diabetes management adherence of patients under the unassisted PCP arm (n = 397) with those under the PCP+DeepDR-LLM arm (n = 372). Patients with newly diagnosed diabetes in the PCP+DeepDR-LLM arm showed better self-management behaviors throughout follow-up (P < 0.05). For patients with referral DR, those in the PCP+DeepDR-LLM arm were more likely to adhere to DR referrals (P < 0.01). Additionally, DeepDR-LLM deployment improved the quality and empathy level of management recommendations. Given its multifaceted performance, DeepDR-LLM holds promise as a digital solution for enhancing primary diabetes care and DR screening., (© 2024. The Author(s).)

Purpose: To evaluate the effects of subthreshold micropulse laser (SML) in addition to anti-vascular endothelial growth factor (VEGF) therapy for diabetic macular edema (DME)., Methods: MEDLINE, EMBASE, and Cochrane Central Register of Controlled Trials were systematically searched for studies that compared anti-VEGF with SML and anti-VEGF monotherapy for DME. Outcome measures were best-corrected visual acuity (BCVA), central macular thickness (CMT), and the number of anti-VEGF injections., Results: Eight studies including 493 eyes were selected. Four studies were randomized controlled, and the other four were retrospective. Meta-analysis showed that there was no significant difference in BCVA (mean difference [MD] -0.04; 95%CI -0.09 to 0.01 logMAR; P = 0.13;). CMT was thinner in the group of anti-VEGF with SML (MD -11.08; 95%CI -21.04 to -1.12 µm; P = 0.03); however, it was due to a single study that weighed higher, and the sensitivity and subcategory analyses did not support the finding. The number of anti-VEGF injections was significantly decreased in the group of anti-VEGF with SML (MD -2.22; 95%CI -3.02 to -1.42; P < 0.0001)., Conclusion: Current evidence indicates that adding SML to anti-VEGF therapy could significantly reduce the number of anti-VEGF injections compared to anti-VEGF monotherapy, while achieve similar BCVA and CMT., (© 2024. The Author(s).)

Background: Given the increasing prevalence of diabetes and diabetic retinopathy (DR) in the UK, this study evaluates a novel primary care optometry-based DR review service against traditional hospital-based virtual DR clinics., Methods: In the hospital-based virtual DR service, patients attended for data capture (visual acuity, fundus photography, macular OCT scanning) with asynchronous review at a later data by a hospital clinician. In the primary care optometry DR review clinic, patients attended an optometry practice for a face-to-face(F2F) review (with imaging) by an optometrist with additional training in DR. Data from both clinic types were analysed. Metrics included DR grading, management plans, grading concordance between primary care optometrists and consultant ophthalmologists, and the assessment of "ungradable" retinopathy referrals., Results: One thousand seven hundred and sixty patients attended the virtual clinic between January 2021 and September 2023. 954 patients attended the primary care review clinic between August 2022 and September 2023. Grading agreements between primary care optometrists and hospital consultants on those patients referred for consultant opinion were significant with Weighted Kappa scores of 0.61(95% CI 0.52-0.69) for DR grade and 0.69(95% CI 0.56-0.82) for diabetic macular oedema (DMO) status. Additionally, the primary care optometry clinic reported a considerably reduced non-attendance rate of 5%, in contrast to 21% in virtual clinics., Conclusion: The primary care optometry-based DR service emerges as an efficient, safe alternative to hospital services. It offers notable advantages over virtual clinics and addresses a care gap for those unsuitable for virtual consultations. The results highlight the potential of primary care-based models in managing DR., (© 2024. The Author(s), under exclusive licence to The Royal College of Ophthalmologists.)

Background: To apply machine learning (ML) algorithms to perform multiclass diabetic retinopathy (DR) classification using both clinical data and optical coherence tomography angiography (OCTA)., Methods: In this cross-sectional observational study, clinical data and OCTA parameters from 203 diabetic patients (203 eye) were used to establish the ML models, and those from 169 diabetic patients (169 eye) were used for independent external validation. The random forest, gradient boosting machine (GBM), deep learning and logistic regression algorithms were used to identify the presence of DR, referable DR (RDR) and vision-threatening DR (VTDR). Four different variable patterns based on clinical data and OCTA variables were examined. The algorithms' performance were evaluated using receiver operating characteristic curves and the area under the curve (AUC) was used to assess predictive accuracy., Results: The random forest algorithm on OCTA+clinical data-based variables and OCTA+non-laboratory factor-based variables provided the higher AUC values for DR, RDR and VTDR. The GBM algorithm produced similar results, albeit with slightly lower AUC values. Leading predictors of DR status included vessel density, retinal thickness and GCC thickness, as well as the body mass index, waist-to-hip ratio and glucose-lowering treatment., Conclusions: ML-based multiclass DR classification using OCTA and clinical data can provide reliable assistance for screening, referral, and management DR populations., (© 2024. The Author(s), under exclusive licence to The Royal College of Ophthalmologists.)

Purpose: To investigate the value of proprotein-converting subtilisin kexin type 9 (PCSK9) levels in type 2 diabetes mellitus (T2D) patients with different stages of diabetic retinopathy (DR) and to compare these findings with a healthy control group without diabetes mellitus (DM)., Methods: A total of 135 patients, 100 of whom were patients with T2D and 35 of whom were in the health control group, were included in this prospective study. T2D patients were divided into three groups: the first group included 34 people with T2D without DR, the second group had 32 people with non-proliferative DR (NPDR), and the third group had 34 people with proliferative DR (PDR). Serum PCSK9 levels were analyzed and compared between the groups., Results: Forty-nine percent of the participants were female, and the mean age was 64 ± 9.1 years, with no statistically significant results between the four groups in terms of age and sex. The mean serum PCSK9 value was significantly different ( p  = 0.01) when all groups were evaluated, and statistically significant change was observed with the progression of DR. When serum PCSK9 levels were evaluated in all T2D patients (groups 1, 2, and 3), a medium-level correlation was observed with low-density lipoprotein ( p  < 0.05)., Conclusion: Serum PCSK9 values differed significantly in diabetic patients compared to the control group. One should be clinically cautious about the usefulness of circulating PCSK9 concentrations as an indicator of the risk of diabetic retinopathy.

Background: Intraocular dexamethasone implant (DEXi) is an efficient treatment for diabetic macular edema (DME). However, it may be unavailable or contraindicated. Triamcinolone acetonide is another corticosteroid that has proved to be safe and effective in treating macular edema complicating various diseases including diabetes. The purpose of this study is to evaluate the outcomes of a switch from DEXi to subtenon triamcinolone acetonide (STTA) and back, in eyes with DME., Methods: Retrospective study. DME eyes that had been treated with DEXi and switched to STTA between October 2018 and February 2019 (stock shortage of DEXi) were included. The functional and anatomical outcomes of the switch and switch-back were studied., Results: 26 eyes of 17 patients (mean age 67.1 ± 8.2 years) were considered. The mean baseline visual acuity (VA) was 0.35 ± 0.17 decimals remaining stable after DEXi, STTA and switch-back to DEXi. The mean central macular thickness (CMT) was 492.7 ± 32.8 µm initially, decreasing to 294.3 ± 133.4 µm after DEXi, 369.9 ± 182.3 µm after STTA and 297.6 ± 72.0 µm after switching back to DEXi (all p < 0.05 versus baseline). Compared to baseline, the CMT reduction was numerically better after DEXi and switching back to DEXi than after STTA (mean reduction: -200.4 µm, -167.7 µm, and -95.08 µm respectively, p = 0.13). Intraocular pressure was comparable after DEXi and STTA., Conclusion: DEXi is the steroid of choice in DME. However, STTA can be a cost-effective alternative when DEXi is unavailable or contraindicated. This study suggests that STTA may be used in the context of a step therapy in DME., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Purpose: To explore variations in systemic and ocular parameters among patients with diabetes, both with and without diabetic peripheral neuropathy (DPN) and to identify sensitive indicators for DPN diagnosis., Methods: Ninty-five patients with type 2 diabetes mellitus (T2DM) were involved in this cross-sectional study, including 49 without DPN and 46 with DPN. Ocular parameters were obtained using optical coherence tomography angiography (OCTA) and corneal confocal microscopy (CCM)., Result: Patients with DPN presented with significantly higher HbA1c (p < 0.05) and glycated albumin (GA, p < 0.01) levels, increased prevalence of diabetic retinopathy (DR, p < 0.05), and lower serum albumin (ALB, p < 0.01) and red blood cell (RBC, p < 0.05) levels. Ocular assessments revealed reduced corneal nerve fiber length (CNFL, p < 0.001) and enlarged foveal avascular zone (FAZ) area (p < 0.05) in DPN group. Logistic regression analysis indicated a significant association of presence of DR, RBC, GA, ALB, CNFL and DPN (p < 0.05, respectively). In the binary logistic regression for DPN risk, all three models including the presence of DR and CNFL exhibited the area under the curve (AUC) exceeding 0.8., Conclusion: The study establishes a strong correlation between ocular parameters and DPN, highlighting CCM's role in early diagnosis. Combining systemic and ocular indicators improves DPN risk assessment and early management., (© 2024. Springer-Verlag Italia S.r.l., part of Springer Nature.)

Objective: Nonalcoholic fatty liver disease (NAFLD) is more prevalent in patients with obesity, diabetes, and metabolic syndrome, which are risk factors for nonalcoholic steatohepatitis and liver fibrosis. NAFLD is related to cardiovascular outcomes in diabetes. We aimed to investigate the relationship between diabetic complications and NAFLD fibrosis score (NFS) and Fibrosis-4 score (FIB-4). Methods: Three hundred patients with type 2 diabetes mellitus (T2DM) were retrospectively evaluated according to NAFLD diagnosis on ultrasound in outpatient clinic. Risk of advanced fibrosis was estimated using FIB-4 and NFS. Diabetic complications of the patients were noted. Results: Presence of diabetic retinopathy is related to FIB-4 ( P = 0.001) and NFS ( P < 0.001) scores. NFS score ( P = 0.037), not FIB-4 ( P = 0.517), is related to diabetic nephropathy. Among macrovascular complications, only coronary artery disease is related to NFS and FIB-4 scores ( P = 0.037 and P = 0.004, respectively). Although we cannot establish any association between fasting blood glucose, glycosylated hemoglobin (HbA1c) values and noninvasive liver fibrosis scores ( P > 0.05), diabetes duration, and age positively correlated with the FIB-4 score ( P = 0.033, P = 0.001). In logistic regression analysis, NFS > 0.676 values are associated with increased rates of diabetic retinopathy, independent of age, sex, HbA1c, and duration diabetes (odds ratio: 1.155, P = 0.030). FIB-4 has no relation with microvascular complications according to logistic regression analysis ( P > 0.05 for all). Neither FIB-4 nor NFS has an effect on the presence of macrovascular complications ( P > 0.05 for all). Conclusion: Our findings suggest that increase in NFS score is associated with the presence of diabetic retinopathy, independent of confounding factors. Further studies are needed on the applicability of noninvasive fibrosis scores in monitoring the presence of diabetic microvascular and macrovascular complications.

Multimodality fusion has gained significance in medical applications, particularly in diagnosing challenging diseases like eye diseases, notably diabetic eye diseases that pose risks of vision loss and blindness. Mono-modality eye disease diagnosis proves difficult, often missing crucial disease indicators. In response, researchers advocate multimodality-based approaches to enhance diagnostics. This study is a unique exploration, evaluating three multimodality fusion strategies-early, joint, and late-in conjunction with state-of-the-art convolutional neural network models for automated eye disease binary detection across three datasets: fundus fluorescein angiography, macula, and combination of digital retinal images for vessel extraction, structured analysis of the retina, and high-resolution fundus. Findings reveal the efficacy of each fusion strategy: type 0 early fusion with DenseNet121 achieves an impressive 99.45% average accuracy. InceptionResNetV2 emerges as the top-performing joint fusion architecture with an average accuracy of 99.58%. Late fusion ResNet50V2 achieves a perfect score of 100% across all metrics, surpassing both early and joint fusion. Comparative analysis demonstrates that late fusion ResNet50V2 matches the accuracy of state-of-the-art feature-level fusion model for multiview learning. In conclusion, this study substantiates late fusion as the optimal strategy for eye disease diagnosis compared to early and joint fusion, showcasing its superiority in leveraging multimodal information., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Purpose: Investigating the relationship between cardiovascular health (CVH) and retinopathy in the adult population of the United States., Design: The cross-sectional study., Methods: The study utilized samples, including the diabetes population, from the National Health and Nutrition Examination Survey (NHANES) conducted between 2005 and 2008 (N= 4249), to assess cardiovascular health (CVH) using the Life's Essential 8 (LE8) assessment. Retinopathy is determined through imaging assessment by professionals independently grading fundus photographs. Univariable and multivariable weighted logistic regression models, restricted cubic splines (RCS), subgroup analysis and weighted quantile sum (WQS) regression approaches were employed to assess the association between LE8 score-based CVH status and retinopathy. The mediation analysis was conducted to investigate whether serum albumin levels mediated the relationship between LE8 score and retinopathy., Results: In a fully adjusted logistic regression model, participants in the moderate and high CVH groups had a 39% (odds ratio (OR) 0.61, 95% confidence interval (CI) 0.43-0.87, P-value = 0.01) and 56% (OR 0.44, 95% CI 0.25-0.77, P-value < 0.001) lower odds of developing retinopathy compared to the low CVH group. The RCS model indicates a significant non-linear relationship between CVH and retinopathy. The WQS regression analysis suggests that blood glucose (47.65%) and blood pressure (19.41%) have the highest weights in relation to retinopathy. Mediation analysis suggests that serum albumin partially mediates the relationship between LE8 scores and retinopathy., Conclusion: This study demonstrates a significant negative correlation between overall cardiovascular health measured by LE8 scores and retinopathy. Public health strategies that promote achieving optimal cardiovascular health indicators may help reduce the burden of retinal microvascular diseases., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

The current diagnosis of diabetic retinopathy is based on fundus images and clinical experience. However, considering the ineffectiveness and non-portability of medical devices, we aimed to develop a diagnostic model for diabetic retinopathy based on glucose series data from the wearable continuous glucose monitoring system. Therefore, this study developed a novel method, i.e., double deep latent autoencoder, for exploring glycemic variability influence from multi-day glucose data for diabetic retinopathy. Specifically, the model proposed in this research could encode continuous glucose sensor data with non-continuous and variable length via the integration of a data reorganization module and a novel encoding module with fragmented-missing-wise objective function. Additionally, the model implements a double deep autoencoder, which integrated convolutional neural network, long short-term memory, to jointly capturing the inter-day and intra-day glucose latent features from glucose series. The effectiveness of the proposed model is evaluated through a cross-validation method to clinical datasets of 765 type 2 diabetes patients. The proposed method achieves the highest accuracy value (0.89), precision value (0.88), and F1 score (0.73). The results suggest that our model can be used to remotely diagnose and screen for diabetic retinopathy by learning potential features of glucose series data collected by wearable continuous glucose monitoring systems., (© 2024. International Federation for Medical and Biological Engineering.)

Objective: To investigate whether the choice of glucose-lowering agent for type 2 diabetes (T2D) impacts a patient's risk of developing sight-threatening diabetic retinopathy complications., Design: Retrospective observational database study emulating an idealized target trial., Subjects: Adult (≥21 years) enrollees in United States commercial, Medicare Advantage, and Medicare fee-for-service plans from January 1, 2014, to December 31, 2021, with T2D and moderate cardiovascular disease (CVD) risk who had no baseline history of advanced diabetic retinal complications, initiating treatment with glucagon-like peptide-1 receptor agonists (GLP-1 RA), sodium-glucose cotransporter 2 inhibitors (SGLT2i), dipeptidyl peptidase-4 inhibitors (DPP-4i), and sulfonylureas., Methods: We used inverse propensity scoring weights in time-to-event Cox proportional hazards models., Main Outcome Measures: Treatment for either diabetic macular edema or proliferative diabetic retinopathy., Results: The final study population included 371 698 patients, of whom 42 265 initiated GLP-1 RA, 53 476 initiated SGLT2i, 78 444 initiated DPP-4i, and 197 513 initiated sulfonylurea agents. The probability of treatment for sight-threatening retinopathy within 2 and 5 years was 0.3% and 0.7% for patients initiating SGLT2i (median follow-up 830 [interquartile range (IQR), 343-1401] days), 0.4% and 1.0% for GLP-1 RA (669 [IQR, 256-1167] days), 0.4% and 0.9% for DPP-4i (1263 [IQR, 688-1938] days), and 0.5% and 1.2% for sulfonylurea (1223 [IQR, 662-1879] days). Sodium-glucose cotransporter 2 inhibitors use was associated with a lower risk of treatment for sight-threatening retinopathy compared with all other medication classes, including GLP-1 RA (hazard ratio [HR], 0.73; 95% confidence interval [CI], 0.55-0.97), DPP-4i (HR, 0.79; 95% CI, 0.64-0.97), and sulfonylurea (HR, 0.61; 95% CI, 0.50-0.74). Glucagon-like peptide-1 receptor agonists use was associated with a similar risk of sight-threatening retinopathy as DPP-4i (HR, 1.07; 95% CI, 0.85-1.35) and sulfonylurea (HR, 0.83; 95% CI, 0.67-1.03)., Conclusions: Sodium-glucose cotransporter 2 inhibitors use was associated with a lower risk of sight-threatening diabetic retinopathy among adults with T2D and moderate CVD risk compared with other glucose-lowering therapies. Glucagon-like peptide-1 receptor agonists do not confer increased retinal risk, relative to DPP-4i and sulfonylurea medications., Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article., (Copyright © 2024 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.)

Purpose: This study aimed to determine the changes in choroidal thickness induced by pioglitazone in diabetic patients., Methods: A total of 261 patients diagnosed with type 2 diabetes who had taken oral pioglitazone for more than 6 months were included in the study. After excluding patients who did not undergo regular eye examinations or who had ophthalmic surgery/interventions during the treatment period, a total of 40 eyes were included. The study examined the duration and dosage of pioglitazone, patient age, ocular axial length, refraction, glycated hemoglobin, systolic blood pressure, corrected visual acuity, macular thickness, choroidal thickness, and choroidal vascular index. Patients were categorized into a high-dose group if their pioglitazone dose was 30 mg or more per day, and a low-dose group if it was 15 mg or less. Choroidal thickness was measured below the subfovea and a 500 µm radius nasal and temporal to that location., Results: Choroidal thickness significantly increased after 6 and 12 months of pioglitazone (6.70 and 13.65 µm, respectively) in all subjects. When stratified by pioglitazone dosage, choroidal thickness increased at 6 and 12 months in both high-dose group (4.48 and 0.84 µm, respectively) and low-dose groups (6.85 and 21.45 µm, respectively), with a greater change observed in the low-dose group (p < 0.05). Based on the location of choroidal thickness measurements, a significant increase in choroidal thickness was observed at 6 and 12 months of pioglitazone treatment in the subfoveal (7.00 and 13.15 µm, respectively) and nasal regions (6.43 and 19.24 µm, respectively), while a significant increase was only observed after 6 months of treatment in the temporal region (8.53 µm, p < 0.05). The largest increase in choroidal thickness was observed in the nasal side., Conclusions: This study found that choroidal thickness increased in diabetic patients after taking pioglitazone. Regular eye examinations are recommended for diabetic patients who are on pioglitazone.

Purpose: To determine the rate of loss to follow up (LTFU) in patients with proliferative diabetic retinopathy (PDR) treated with anti-VEGF therapy and/or panretinal photocoagulation (PRP) in the United States., Design: Retrospective cohort study using the national IRIS® (Intelligent Research in Sight) Registry data., Subjects: A total of 73 595 eyes of 56 590 patients with PDR diagnosed between 2013 and 2015 and treated between 2013 and 2018., Methods: Multivariable logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs)., Main Outcome Measures: Loss to follow up was no follow up within 12 months from last treatment., Results: For patient eyes treated for PDR, 11.7% (95% CI, 11.5-12.0) were LTFU. Among patients with PDR treated with anti-VEGF therapy alone, PRP alone, and anti-VEGF and PRP, the rates of LTFU were 12.3% (95% CI, 11.8-12.7), 12.6% (95% CI, 12.1-13.0), and 10.8% (95% CI, 10.4-11.1), respectively. Risk factors for LTFU include Black or African American race/ethnicity (odds ratio [OR], 1.26; 95% CI, 1.13-1.41; P < 0.001), Hispanic ethnicity (OR, 1.28; 95% CI, 1.16-1.42; P < 0.001), Native American/Alaska Native or Native Hawaiian/Other Pacific Islander race/ethnicity (OR, 2.69; 95% CI, 2.14-3.38; P < 0.001), and unilateral disease (OR, 2.05; CI, 1.88-2.23; P < 0.001). Odds for LTFU were higher with patients with baseline vision of 20/50 to 20/200 (OR, 1.25; 95% CI, 1.15-1.36; P < 0.001) and with vision worse than 20/200 (OR, 1.22; 95% CI, 1.05-1.42; P = 0.01) than for patient eyes with a baseline visual acuity of 20/40 or better. Odds for LTFU were lower for Medicare Fee-for-Service (OR, 0.71; 95% CI, 0.64-0.79; P < 0.001) and Medicare Managed (OR, 0.66; 95% CI, 0.56-0.78; P < 0.001) compared with private insurance. Odds for LTFU were lower for patients treated in the Midwest (OR, 0.72; 95% CI, 0.64-0.81; P < 0.001) and West (OR, 0.83; 95% CI, 0.74-0.94; P = 0.003) compared with in the South region., Conclusions: The rate of LTFU is between 10% and 12% among patients with PDR who were treated with anti-VEGF injections and/or PRP. Risk factors include Black or African American race/ethnicity, Hispanic ethnicity, baseline vision worse than 20/40, private insurance, South region, and unilateral disease., Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article., (Copyright © 2024 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.)

Background/objectives: To assess the relationship between macular vessel density metrics and foveal avascular zone (FAZ) characteristics on optical coherence tomography angiography (OCTA) and lesion distribution in eyes with diabetic retinopathy (DR)., Subjects/methods: Patients with DR who underwent both Optos ultrawidefield (UWF) pseudocolor imaging and macular OCTA (Cirrus Angioplex, 6 × 6 mm) were included in this cross-sectional observational study. The distribution of DR lesions was assessed by comparing each of the peripheral ETDRS extended fields (3-7) against their corresponding ETDRS field, hence eyes were defined as either having predominantly peripheral lesions (PPL) or predominantly central lesions (PCL). En face OCTA images from the superficial and deep capillary plexuses (SCP and DCP) were then analysed using Image J software. Perfusion density (PD), vessel length density (VLD), and fractal dimensions (FD) were calculated following binarization and skeletonization of the images., Results: Out of 344 eyes, 116 (33.72%) eyes had PPL and 228 (66.28%) eyes had PCL. For all DRSS levels, VLD, PD, and FD were not significantly different between eyes with PPL and PCL. The FAZ in eyes with PPL, however, was found to be more circular in shape compared to eyes with PCL (p = 0.037)., Conclusion: Although the presence of PPL has been associated with a higher risk for diabetic retinopathy progression, the macular perfusion is similar in eyes with PPL and PCL. The FAZ is more circular in eyes with PPL, but the clinical relevance of this difference remains to be defined., (© 2024. The Author(s).)

Background: There has been a paucity of evidence for the development of a prediction model for diabetic retinopathy (DR) in Ethiopia. Predicting the risk of developing DR based on the patient's demographic, clinical, and behavioral data is helpful in resource-limited areas where regular screening for DR is not available and to guide practitioners estimate the future risk of their patients., Methods: A retrospective follow-up study was conducted at the University of Gondar (UoG) Comprehensive Specialized Hospital from January 2006 to May 2021 among 856 patients with type 2 diabetes (T2DM). Variables were selected using the Least Absolute Shrinkage and Selection Operator (LASSO) regression. The data were validated by 10-fold cross-validation. Four ML techniques (naïve Bayes, K-nearest neighbor, decision tree, and logistic regression) were employed. The performance of each algorithm was measured, and logistic regression was a well-performing algorithm. After multivariable logistic regression and model reduction, a nomogram was developed to predict the individual risk of DR., Results: Logistic regression was the best algorithm for predicting DR with an area under the curve of 92%, sensitivity of 87%, specificity of 83%, precision of 84%, F1-score of 85%, and accuracy of 85%. The logistic regression model selected seven predictors: total cholesterol, duration of diabetes, glycemic control, adherence to anti-diabetic medications, other microvascular complications of diabetes, sex, and hypertension. A nomogram was developed and deployed as a web-based application. A decision curve analysis showed that the model was useful in clinical practice and was better than treating all or none of the patients., Conclusions: The model has excellent performance and a better net benefit to be utilized in clinical practice to show the future probability of having DR. Identifying those with a higher risk of DR helps in the early identification and intervention of DR., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

To investigate the variability in subthreshold laser treatment plans for patients with diabetic macular edema or central serous chorioretinopathy. Diagnostic images from 20 patients were utilized, and 25 retina specialists generated subthreshold treatment plans along with a self-rated experience level. Evaluation comprised of i) Area Variability (AV): quantifies the consensus regarding the covered area and is the difference between the areas planned by 75% and 25% of the participants ii) mean Centroid Distance (CD): level of agreement on the localization of a treatment. Subgroup analysis investigated the impact of participants' experience levels, utilizing the Mann-Whitney-Wilcoxon test. The predominant plan style is a targeted treatment approach (92%) and avoidance of subfoveal region (89%). Mean CD is 71.0 ± 37.5 pixels (≈half disc diameter) and mean AV is 9.8%±8.9%. A slight difference is observed between the 50-75% areas, but a notable distinction exists between the 25-50% areas. Subgroup analysis revealed CD and AV value of 75.9 pixels and 24% in the lower experience level group as opposed to 55.9 pixels and 8.6% in the higher experience level group. There is significant variability in treatment planning which reduces with increased experience of retina specialists. While consensus is observed around focal points, differences in the surrounding extents persist., (© 2024. The Author(s).)

Purpose: The aim of this study is to investigate the effect of vitreomacular interface disorders (VMID) on treatment response in patients treated with anti-vascular endothelial growth factor (anti-VEGF) due to diabetic macular edema (DME)., Methods: Three hundred seventy-seven eyes of 239 patients in the MARMASIA Study Group who received intravitreal anti-VEGF treatment (IVT) due to DME were included in the study. The group 1 consisted of 44 eyes of the patients who had not received any treatment before, were followed up regularly for 24 months after at least a 3-month loading dose, and suffered from VMID such as epiretinal membrane, vitreomacular adhesion or traction, and lamellar hole. The group 2 consisted of 333 eyes of the patients without VMID. Best-corrected visual acuity (BCVA) and central macular thickness (CMT) of the patients at baseline, 3rd month, 6th month, 1st year and 2nd year follow-ups were measured., Results: The mean age of the Groups 1 and 2 was 67.1 ± 11.3 and 61.9 ± 10.2 years, respectively. 61.3% of the group 1 and 58.8% of the group 2 were female (p > 0.05). The duration of diabetes was 19.2 ± 3.7 and 15.8 ± 3.2 years, respectively, and the number of follow-ups was 16.09 ± 4.68 and 12.06 ± 4.58, respectively in the groups (p < 0.001, 0.001, respectively). The number of IVT was 7.13 ± 2.71 and 7.20 ± 2.22, respectively in the groups 1 and 2 and no statistically significant difference was observed between them (p = 0.860). According to logMAR, BCVA values at baseline were 0.63 ± 0.24 and 0.59 ± 0.26 (p = 0.29), respectively, in the groups and the amount of change in BCVA at the end of the 2nd year was - 0.02 ± 0.48 in the group 1 and - 0.12 ± 0.48 in the group 2. It was observed as 0.48 (p = 0.13). Although the increase in BCVA was greater at all follow-ups in the group 2 compared to their initial examination, no significant difference was observed between the groups in terms of BCVA change. The CMT values of the groups at baseline were 442.5 ± 131.3 µm and 590.9 ± 170.6 µm, respectively (p = 0.03) The decrease in CMT after IVT was significantly greater in the group 2 at all follow-ups when compared to the first group (p < 0.05)., Conclusion: While the presence of VMID in DME patients receiving IVT did not affect visual results, it negatively affected the anatomical response and macular edema morphology. The presence of VMID at baseline affected the success of IVT. It should be taken into consideration that VMID may resolve spontaneously or with IVT, and new cases of VMID may occur in patients during the treatment process., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

Objective: The long-term glucose monitoring is essential to the risk assessment of diabetic retinopathy (DR), the aim of this study was to investigate the predictive ability of visit-to-visit fasting blood glucose (FBG) indices on the risk of DR., Methods: This was a community-based, cohort study conducted from 2013 to 2021. DR was diagnosed by digital fundus photography. The FPG indices included FBG, var. Associations of each FBG indices and DR were estimated using multinomial logistic regression models adjusting for confounders, and discrimination was determined by area under the curve (AUC). Predictive utility of different models was compared by changes in AUC, integrated discrimination improvement (IDI), and net reclassification index (NRI)., Results: This study analyzed 5054 participants, the mean age was 46.26 ± 11.44 years, and 2620 (51.84%) were women. After adjustment for confounders, the adjusted odds ratios (ORs) with 95% confidence intervals (CIs) for FBG, SD, CV, VIM, ARV, M-FBG, and cumulative FBG load were 1.62 (1.52-1.73), 2.74 (2.38-3.16), 1.78 (1.62-1.95), 1.11 (0.95-1.29), 1.72 (1.56-1.91), 2.15 (1.96-2.36), and 2.57 (2.31-2.85), respectively. The AUC of the model with separate cumulative FBG load and classical risk factors was 0.9135 (95%CI 0.8890-0.9380), and no substantive improvement in discrimination was achieved with the addition of other FBG indices once cumulative FBG load was in the model., Conclusions: Cumulative FBG load is adequate for capturing the glucose-related DR risk, and the predictive utility of cumulative FBG load is not significantly improved by adding or replacing other FBG indices in the assessment of DR risk., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Ju, Guo, Ai, Yang, Zhu, Shi, Li, Yu, Xiao, Su, Yan, Li, Lian, Wang, Ding, Wang, Lu, Cui and Li.)

Competing Interests: Competing interests: None declared.

Background/aims: To compare the performance of generative versus retrieval-based chatbots in answering patient inquiries regarding age-related macular degeneration (AMD) and diabetic retinopathy (DR)., Methods: We evaluated four chatbots: generative models (ChatGPT-4, ChatGPT-3.5 and Google Bard) and a retrieval-based model (OcularBERT) in a cross-sectional study. Their response accuracy to 45 questions (15 AMD, 15 DR and 15 others) was evaluated and compared. Three masked retinal specialists graded the responses using a three-point Likert scale: either 2 (good, error-free), 1 (borderline) or 0 (poor with significant inaccuracies). The scores were aggregated, ranging from 0 to 6. Based on majority consensus among the graders, the responses were also classified as 'Good', 'Borderline' or 'Poor' quality., Results: Overall, ChatGPT-4 and ChatGPT-3.5 outperformed the other chatbots, both achieving median scores (IQR) of 6 (1), compared with 4.5 (2) in Google Bard, and 2 (1) in OcularBERT (all p ≤8.4×10 -3 ). Based on the consensus approach, 83.3% of ChatGPT-4's responses and 86.7% of ChatGPT-3.5's were rated as 'Good', surpassing Google Bard (50%) and OcularBERT (10%) (all p ≤1.4×10 -2 ). ChatGPT-4 and ChatGPT-3.5 had no 'Poor' rated responses. Google Bard produced 6.7% Poor responses, and OcularBERT produced 20%. Across question types, ChatGPT-4 outperformed Google Bard only for AMD, and ChatGPT-3.5 outperformed Google Bard for DR and others., Conclusion: ChatGPT-4 and ChatGPT-3.5 demonstrated superior performance, followed by Google Bard and OcularBERT. Generative chatbots are potentially capable of answering domain-specific questions outside their original training. Further validation studies are still required prior to real-world implementation., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Objective: This study aims to analyze the application and clinical translation value of the self-evolving machine learning methods in predicting diabetic retinopathy and visualizing clinical outcomes., Methods: A retrospective study was conducted on 300 diabetic patients admitted to our hospital between January 2022 and October 2023. The patients were divided into a diabetic retinopathy group (n=150) and a non-diabetic retinopathy group (n=150). The improved Beetle Antennae Search (IBAS) was used for hyperparameter optimization in machine learning, and a self-evolving machine learning model based on XGBoost was developed. Value analysis was performed on the predictive features for diabetic retinopathy selected through multifactor logistic regression analysis, followed by the construction of a visualization system to calculate the risk of diabetic retinopathy occurrence., Results: Multifactor logistic regression analysis revealed that being male, having a longer disease duration, higher systolic blood pressure, fasting blood glucose, glycosylated hemoglobin, low-density lipoprotein cholesterol, and urine albumin-to-creatinine ratio were risk factors for the development of diabetic retinopathy, while non-pharmacological treatment was a protective factor. The self-evolving machine learning model demonstrated significant performance advantages in early diagnosis and prediction of diabetic retinopathy occurrence., Conclusion: The application of the self-evolving machine learning models can assist in identifying features associated with diabetic retinopathy in clinical settings, enabling early prediction of disease occurrence and aiding in the formulation of treatment plans to improve patient prognosis., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Li, Hu, Zhang, Li, Tang and Chen.)

Background: Labeling color fundus photos (CFP) is an important step in the development of artificial intelligence screening algorithms for the detection of diabetic retinopathy (DR). Most studies use the International Classification of Diabetic Retinopathy (ICDR) to assign labels to CFP, plus the presence or absence of macular edema (ME). Images can be grouped as referrable or nonreferrable according to these classifications. There is little guidance in the literature about how to collect and use metadata as a part of the CFP labeling process., Objective: This study aimed to improve the quality of the Multimodal Database of Retinal Images in Africa (MoDRIA) by determining whether the availability of metadata during the image labeling process influences the accuracy, sensitivity, and specificity of image labels. MoDRIA was developed as one of the inaugural research projects of the Mbarara University Data Science Research Hub, part of the Data Science for Health Discovery and Innovation in Africa (DS-I Africa) initiative., Methods: This is a crossover assessment with 2 groups and 2 phases. Each group had 10 randomly assigned labelers who provided an ICDR score and the presence or absence of ME for each of the 50 CFP in a test image with and without metadata including blood pressure, visual acuity, glucose, and medical history. Specificity and sensitivity of referable retinopathy were based on ICDR scores, and ME was calculated using a 2-sided t test. Comparison of sensitivity and specificity for ICDR scores and ME with and without metadata for each participant was calculated using the Wilcoxon signed rank test. Statistical significance was set at P<.05., Results: The sensitivity for identifying referrable DR with metadata was 92.8% (95% CI 87.6-98.0) compared with 93.3% (95% CI 87.6-98.9) without metadata, and the specificity was 84.9% (95% CI 75.1-94.6) with metadata compared with 88.2% (95% CI 79.5-96.8) without metadata. The sensitivity for identifying the presence of ME was 64.3% (95% CI 57.6-71.0) with metadata, compared with 63.1% (95% CI 53.4-73.0) without metadata, and the specificity was 86.5% (95% CI 81.4-91.5) with metadata compared with 87.7% (95% CI 83.9-91.5) without metadata. The sensitivity and specificity of the ICDR score and the presence or absence of ME were calculated for each labeler with and without metadata. No findings were statistically significant., Conclusions: The sensitivity and specificity scores for the detection of referrable DR were slightly better without metadata, but the difference was not statistically significant. We cannot make definitive conclusions about the impact of metadata on the sensitivity and specificity of image labels in our study. Given the importance of metadata in clinical situations, we believe that metadata may benefit labeling quality. A more rigorous study to determine the sensitivity and specificity of CFP labels with and without metadata is recommended., (©Simon Arunga, Katharine Elise Morley, Teddy Kwaga, Michael Gerard Morley, Luis Filipe Nakayama, Rogers Mwavu, Fred Kaggwa, Julius Ssempiira, Leo Anthony Celi, Jessica E Haberer, Celestino Obua. Originally published in JMIR Formative Research (https://formative.jmir.org), 18.09.2024.)

Purpose: The purpose of the study was to evaluate the status of health services for diabetes mellitus (DM) and diabetic retinopathy (DR) in Jordan in terms of availability and accessibility by interviewing people engaged with health-care process including decision makers, professional providers, and community representatives., Methods: This is a qualitative study where participants were invited to respond by direct interview to a standard questionnaire that is validated by the World Health Organization (WHO), "Tool for Assessment of Diabetic Retinopathy and DM Management Systems". The questionnaire casts out of the WHO Health Systems Framework concept, assessing the presence of programs, guidelines, health technology and workforce, health promotion, and financing of DM and DR care in Jordan., Results: Forty-two participants were included. DM is considered as priority in Jordan, but no programs are implemented. National guidelines are present for DM but not for DR. Networking should be improved between different disciplines caring for DM and DR. Human resources for DM and DR care were found insufficient and inadequately distributed. Health technology is only present in major cities and central hospitals. There is no nationwide data on disease burden nor a screening program for DR. More collaborated efforts should be spent on health education and raising awareness among people living with diabetes about the risk of DR., Conclusion: Advanced DM and DR care is not accessible to most people. Programmatic efforts from the government and NGOs must formulate a national action plan to reduce the human and financial impact of the disease in Jordan., Competing Interests: There are no conflicts of interest., (Copyright: © 2024 Middle East African Journal of Ophthalmology.)

Purpose: The purpose of this study was to report the visual and anatomic results at 12 and 24 months using the protocol of 3 monthly pro re nata (PRN) injections for diabetic macular edema (DME)., Methods: This was a retrospective chart review of 97 eyes with DME treated with a protocol of cycles consisting of 3 monthly injections of anti-vascular endothelial growth factor drugs on a PRN basis. Change in visual acuity was the main outcome measure. Macular thickness, number of injections, cycles, and visits in years 1 and 2 of follow-up were secondary outcomes., Results: Ninety-six patients with a mean age of 60.9 ± 9.96 years were followed for a mean of 22.17 ± 12.30 months. Ninety-two (95.9%), 3 (3%), and 1 (1%) patients were started on bevacizumab, ranibizumab, and aflibercept, respectively. Of bevacizumab patients, 17 (18.2%) were eventually switched to aflibercept. The mean 12-month improvement (standard deviation [SD]) was + 3.3 (17.4) letters (95% confidence interval [CI] = +0.36-+7.05, P < 0.001) after an average (SD) of 5.97 (2.98) injections over a mean (SD) of 1.7 (1.0) three-injection cycles. The mean 24-month improvement (SD) was + 5.6 (13.0) letters (95% CI: -0.28-11.05, P = 0.0186) after an average (SD) of 8.72 (6.31) injections over a mean (SD) of 2.9 (2.1) three-injection cycles. The mean central macular thickness (SD) at baseline, 12 months, and 24 months was 374 ± 120, 322 ± 88, and 305 ± 70 µm. Optical coherence tomography was fluid free at 12 and 24 months in 27.6% and 46% of eyes, respectively., Conclusion: Comparable to real-world studies, this protocol can stabilize or improve vision in more than 85% of DME patients over 24 months. The most important factor in improvement of vision is increasing number of injections and visits., Competing Interests: There are no conflicts of interest., (Copyright: © 2024 Middle East African Journal of Ophthalmology.)

Objective: To develop a prediction model for the risk of diabetic retinopathy (DR) in patients with newly diagnosed type 2 diabetes mellitus (T2DM). Methods: Patients with new diagnosis of T2DM recorded in Yinzhou Regional Health Information Platform between January 1, 2015 and December 31, 2022 were included in the study. The predictor variables were selected by using Lasso-Cox proportional hazards regression model. Cox proportional hazards regression models were used to establish the prediction model for the risk of DR. Bootstrap method (500 resamples) was used for internal validation, and the performance of the model was assessed by C-index, the receiver operating characteristic curve and area under the curve (AUC), and calibration curve. Results: The predictor variables included in the final model were age of T2DM onset, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, estimated glomerular filtration rate, and history of lipid-lowering agent and angiotensin converting enzyme inhibitor uses. The C-index of the final model was 0.622, and the mean corrected C-index was 0.623 (95% CI : 0.607-0.634). The AUC values for predicting the risk of DR after 3, 5, and 7 years were 0.631, 0.620, and 0.624, respectively, with a high degree of overlap of the calibration curves with the ideal curves. Conclusion: In this study, a simple and practical risk prediction model for DR risk prediction was developed, which could be used as a reference for individualized DR screening and intervention in newly diagnosed T2DM patients.

Objectives: To identify the barriers and facilitators to the successful implementation of imaging-based diagnostic artificial intelligence (AI)-assisted decision-making software in China, using the updated Consolidated Framework for Implementation Research (CFIR) as a theoretical basis to develop strategies that promote effective implementation., Design: This qualitative study involved semistructured interviews with key stakeholders from both clinical settings and industry. Interview guide development, coding, analysis and reporting of findings were thoroughly informed by the updated CFIR., Setting: Four healthcare institutions in Beijing and Shanghai and two vendors of AI-assisted decision-making software for lung nodules detection and diabetic retinopathy screening were selected based on purposive sampling., Participants: A total of 23 healthcare practitioners, 6 hospital informatics specialists, 4 hospital administrators and 7 vendors of the selected AI-assisted decision-making software were included in the study., Results: Within the 5 CFIR domains, 10 constructs were identified as barriers, 8 as facilitators and 3 as both barriers and facilitators. Major barriers included unsatisfactory clinical performance (Innovation); lack of collaborative network between primary and tertiary hospitals, lack of information security measures and certification (outer setting); suboptimal data quality, misalignment between software functions and goals of healthcare institutions (inner setting); unmet clinical needs (individuals). Key facilitators were strong empirical evidence of effectiveness, improved clinical efficiency (innovation); national guidelines related to AI, deployment of AI software in peer hospitals (outer setting); integration of AI software into existing hospital systems (inner setting) and involvement of clinicians (implementation process)., Conclusions: The study findings contributed to the ongoing exploration of AI integration in healthcare from the perspective of China, emphasising the need for a comprehensive approach considering both innovation-specific factors and the broader organisational and contextual dynamics. As China and other developing countries continue to advance in adopting AI technologies, the derived insights could further inform healthcare practitioners, industry stakeholders and policy-makers, guiding policies and practices that promote the successful implementation of imaging-based diagnostic AI-assisted decision-making software in healthcare for optimal patient care., Competing Interests: Competing interests: JZ is an employee of MSD R&D (China). LL is affiliated with Merck Sharp & Dohme, a subsidiary of Merck & Co., Rahway, New Jersey, USA, which funded the study and monitored the conduct of the study., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objective: This study seeks to explain the relationship between systemic conditions and hard exudate formations in diabetic macular edema patients. Besides, the study aimed to quantitatively examine changes in the area, location, and impact on visual function of hard exudates following intravitreal dexamethasone implant injections., Methods: A retrospective analysis was conducted, including 40 patients (40 eyes) diagnosed with non-proliferative diabetic retinopathy and concurrent macular edema between January 1, 2022, and January 1, 2024. Preoperative evaluations included glycated hemoglobin, lipid profile, and renal function examinations. Based on the location of HE, patients were divided into two groups: Group A, with HE in 1 mm of the central fovea, and Group B, with HE outside 1 mm of the central fovea. Selected eyes were subject to pre- and postoperative examinations, including best-corrected visual acuity (BCVA), intraocular pressure, slit-lamp biomicroscopy, scanning laser ophthalmoscopy (SLO), optical coherence tomography, and multifocal electroretinography. Following screening and examination, patients received an immediate intravitreal injection of the DEX implant, with an injection administered at the four-month mark. Hard exudate (HE) areas were measured utilizing SLO fundus imaging., Results: Total cholesterol, low-density lipoprotein, and triglyceride levels were found to be positively correlated with the presence of HE. Following surgical intervention, all patients demonstrated an improvement in BCVA. The mean BCVA increased from a preoperative measurement of 0.79 ± 0.04 to 0.39 ± 0.02 at the 6 month follow-up, indicating a statistically significant difference (p < 0.001). The baseline HE area for the entire patient cohort was 2.28 ± 0.22. One month post-operation, the HE area exhibited a slight increase to 2.27 ± 0.22. However, by the 6 month follow-up, the HE area had significantly decreased to 0.8 ± 0.87, representing a 35.09% reduction from the baseline measurement (p < 0.001). It is worth noting that Patient P1 did not exhibit a statistically significant difference between preoperative and six-month postoperative HE area (p = 0.032). Preoperative BCVA measurements for Group A and Group B were 0.81 ± 0.03 and 0.77 ± 0.03, respectively, with no statistically significant intergroup difference (p = 0.333). The baseline HE area for Group A was 2.61 ± 0.16, which decreased to 0.38 ± 0.20 at the six-month follow-up, representing a 14.60% reduction from the baseline total area. For Group B, the baseline HE area was measured at 1.95 ± 0.09, then decreasing to 1.21 ± 0.13 at the six-month follow-up, indicating a 62.05% reduction from the baseline total area. A statistically significant difference in the postoperative 6 month HE area was observed between Group A and Group B (p < 0.001). In Group A, the reduction in HE area (initial HE area-final HE area) was positively correlated with the improvement in P1 (initial P1-final P1) (r = 0.610, p = 0.004). In Group B, a similar positive correlation was found (initial HE area-final HE area with initial P1-final P1) (r = 0.488, p = 0.029). In Group B, the reduction in HE area (initial HE area-final HE area) correlated positively with the improvement in BCVA (initial BCVA-final BCVA) (r = 0.615, p = 0.004). Additionally, in Group B, the reduction in HE area (initial HE area-final HE area) was positively correlated with the improvement in CMT (initial CMT-final CMT) (r = -0.725, p< 0.001). Aggravated cataracts were observed in thirteen eyes during a follow-up examination 6 months later., Conclusion: HE formation is associated with lipid levels. Dexamethasone implants demonstrate effectiveness in reducing HE areas in the short term, reducing macular edema, improving retinal structure, and enhancing visual function. The incidence of postoperative complications such as cataracts and glaucoma remains low., (© 2024. The Author(s).)

Diabetic retinopathy (DR) is the most common complication in patients with diabetes, and screening for sight-threatening end-stages is indicated to avoid severe visual loss. Screening of DR is nationally implemented in Denmark according to evidence-based national guidelines, which includes the use of individualised screening intervals. Treatment is indicated for proliferative DR and diabetic macular oedema, and treatment modalities include retinal laser therapy, intravitreal angiostatic therapy, and vitrectomy. In this review, we summarise the current guidelines for screening and treatment of DR in Denmark., (Published under Open Access CC-BY-NC-BD 4.0. https://creativecommons.org/licenses/by-nc-nd/4.0/.)