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Purpose: Experimental investigation in human eyelids to confirm that exposing excised tarsal plates to ultraviolet-A radiation can induce a stiffening effect through the riboflavin-photosensitized crosslinking of tarsal collagen., Methods: Thirteen tarsal plates excised from nonfrozen human cadavers were irradiated with ultraviolet-A rays (365 nm wavelength) at an irradiance of 75 mW/cm 2 for 3 minutes, equivalent to a radiation fluence of 13.5 J/cm 2 , in the presence of a riboflavin derivative as a photosensitizer. The tensile stress (strength) and Young's modulus (stiffness) of both nonirradiated and irradiated specimens were measured with the BioTester 5000 in the uniaxial mode at a strain of 10% and analyzed statistically. Individual specimens excised from 2 cadavers were also examined by routine histopathologic protocols to assess the effect of radiation on the Meibomian glands and collagen organization., Results: The irradiation enhanced both stiffness and strength of the human tarsal specimens, the difference between the test samples and controls being statistically significant ( p < 0.0002 for n = 13). Histology indicated no damage to tarsal connective tissue or to Meibomian glands, and revealed a more compact packing of the collagen network located around the glands, which may be beneficial. The existence of collagen compaction was also supported by the reduction of samples' thickness after irradiation ( p = 0.0645)., Conclusions: The irradiation of tarsal tissue with ultraviolet-A light of tarsus appears to be a safe and effective method for reducing eyelid laxity in human patients., Competing Interests: U.S. Patent No. 11420073 (B2) and Australian Patent No. AU 2018201200 B2 have been granted for the method related to this research, and have been assigned to the Queensland Eye Institute Foundation by the authors S.S. and T.V.C. The remaining authors have no conflicts of interest to disclose., (Copyright © 2024 The American Society of Ophthalmic Plastic and Reconstructive Surgery, Inc.)

Patient compliance in chronic illnesses is essential for disease management. This also applies to age-related macular degeneration (AMD), a chronic acquired retinal degeneration that needs constant monitoring and patient cooperation. Therefore, patients with AMD can benefit by being properly informed about their disease, regardless of the condition's stage. Information is essential in keeping them compliant with lifestyle changes, regular monitoring, and treatment. Large language models have shown potential in numerous fields, including medicine, with remarkable use cases. In this paper, we wanted to assess the capacity of two large language models (LLMs), ChatGPT4 and PaLM2, to offer advice to questions frequently asked by patients with AMD. After searching on AMD-patient-dedicated websites for frequently asked questions, we curated and selected a number of 143 questions. The questions were then transformed into scenarios that were answered by ChatGPT4, PaLM2, and three ophthalmologists. Afterwards, the answers provided by the two LLMs to a set of 133 questions were evaluated by two ophthalmologists, who graded each answer on a five-point Likert scale. The models were evaluated based on six qualitative criteria: (C1) reflects clinical and scientific consensus, (C2) likelihood of possible harm, (C3) evidence of correct reasoning, (C4) evidence of correct comprehension, (C5) evidence of correct retrieval, and (C6) missing content. Out of 133 questions, ChatGPT4 received a score of five from both reviewers to 118 questions (88.72%) for C1, to 130 (97.74%) for C2, to 131 (98.50%) for C3, to 133 (100%) for C4, to 132 (99.25%) for C5, and to 122 (91.73%) for C6, while PaLM2 to 81 questions (60.90%) for C1, to 114 (85.71%) for C2, to 115 (86.47%) for C3, to 124 (93.23%) for C4, to 113 (84.97%) for C5, and to 93 (69.92%) for C6. Despite the overall high performance, there were answers that are incomplete or inaccurate, and the paper explores the type of errors produced by these LLMs. Our study reveals that ChatGPT4 and PaLM2 are valuable instruments for patient information and education; however, since there are still some limitations to these models, for proper information, they should be used in addition to the advice provided by the physicians.

(1) Background: Diabetes mellitus (DM) is a growing challenge, both for patients and physicians, in order to control the impact on health and prevent complications. Millions of patients with diabetes require medical attention, which generates problems regarding the limited time for screening but also addressability difficulties for consultation and management. As a result, screening programs for vision-threatening complications due to DM have to be more efficient in the future in order to cope with such a great healthcare burden. Diabetic macular edema (DME) is a severe complication of DM that can be prevented if it is timely screened with the help of optical coherence tomography (OCT) devices. Newly developing state-of-the-art artificial intelligence (AI) algorithms can assist physicians in analyzing large datasets and flag potential risks. By using AI algorithms in order to process OCT images of large populations, the screening capacity and speed can be increased so that patients can be timely treated. This quick response gives the physicians a chance to intervene and prevent disability. (2) Methods: This study evaluated ConvNeXt and EfficientNet architectures in correctly identifying DME patterns on real-life OCT images for screening purposes. (3) Results: Firstly, we obtained models that differentiate between diabetic retinopathy (DR) and healthy scans with an accuracy of 0.98. Secondly, we obtained a model that can indicate the presence of edema, detachment of the subfoveolar neurosensory retina, and hyperreflective foci (HF) without using pixel level annotation. Lastly, we analyzed the extent to which the pretrained weights on natural images "understand" OCT scans. (4) Conclusions: Pretrained networks such as ConvNeXt or EfficientNet correctly identify features relevant to the differentiation between healthy retinas and DR, even though they were pretrained on natural images. Another important aspect of our research is that the differentiation between biomarkers and their localization can be obtained even without pixel-level annotation. The "three biomarkers model" is able to identify obvious subfoveal neurosensory detachments, retinal edema, and hyperreflective foci, as well as very small subfoveal detachments. In conclusion, our study points out the possible usefulness of AI-assisted diagnosis of DME for lowering healthcare costs, increasing the quality of life of patients with diabetes, and reducing the waiting time until an appropriate ophthalmological consultation and treatment can be performed.

Aim: The aim of this study was to evaluate the visual outcome of lens dislocation and subluxation managed by pars plana vitrectomy (PPV) and lensectomy in patients with open (OGIs) or closed globe injuries (CGIs)., Methods: Medical records of 70 consecutive patients treated by PPV and lensectomy over a period of 11 years (1 January 2010-31 December 2020) were retrospectively reviewed. We collected demographic data, best corrected visual acuity (BCVA) using a Snellen Chart pre- and postoperatively, associated ocular injuries and treatment strategy. Visual outcome was evaluated according to the final BCVA which was defined as poor <0.1 or good ≥0.1., Results: The mean age was 57.9 ± 17.6 years. CGIs were present in 49 (70.0%) cases and open OGIs in 21 (30.0%) cases. The dislocation mechanism was zonular lysis in 59 cases (84.3%) and capsular rupture in 11 cases (15.7%). The intraocular lens implant (IOL) was sutured to the sclera in 51 (72.9%) cases or positioned in the capsular bag or in the sulcus in 3 (4.3%) cases and 1 (1.4%) case, respectively, whereas 15 (21.4%) patients remained aphakic. A good BCVA ≥ 0.1 was achieved in 45.71% of the eyes. The presence of retinal detachment (RD) ( p = 0.014), iridodonesis ( p = 0.011) and initial BCVA ( p = 0.000) achieved statistical significance in predicting visual outcome. After treatment, 45.71% of patients achieved a final BCVA ≥ 0.1., Conclusion: RD, iridodonesis and initial BCVA were risk factors for poor visual outcome in our series.

The era of artificial intelligence (AI) has revolutionized our daily lives and AI has become a powerful force that is gradually transforming the field of medicine. Ophthalmology sits at the forefront of this transformation thanks to the effortless acquisition of an abundance of imaging modalities. There has been tremendous work in the field of AI for retinal diseases, with age-related macular degeneration being at the top of the most studied conditions. The purpose of the current systematic review was to identify and evaluate, in terms of strengths and limitations, the articles that apply AI to optical coherence tomography (OCT) images in order to predict the future evolution of age-related macular degeneration (AMD) during its natural history and after treatment in terms of OCT morphological structure and visual function. After a thorough search through seven databases up to 1 January 2022 using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 1800 records were identified. After screening, 48 articles were selected for full-text retrieval and 19 articles were finally included. From these 19 articles, 4 articles concentrated on predicting the anti-VEGF requirement in neovascular AMD (nAMD), 4 articles focused on predicting anti-VEGF efficacy in nAMD patients, 3 articles predicted the conversion from early or intermediate AMD (iAMD) to nAMD, 1 article predicted the conversion from iAMD to geographic atrophy (GA), 1 article predicted the conversion from iAMD to both nAMD and GA, 3 articles predicted the future growth of GA and 3 articles predicted the future outcome for visual acuity (VA) after anti-VEGF treatment in nAMD patients. Since using AI methods to predict future changes in AMD is only in its initial phase, a systematic review provides the opportunity of setting the context of previous work in this area and can present a starting point for future research.

Acquired vitelliform lesions (AVLs) are associated with a large spectrum of retinal diseases, among which is age-related macular degeneration (AMD). The purpose of this study was to characterize AVLs' evolution in AMD patients using optical coherence tomography (OCT) technology and ImageJ software. We measured AVLs' size and density and followed their impacts over surrounding retinal layers. Average retinal pigment epithelium (RPE) thickness in the central 1 mm quadrant (45.89 ± 27.84 µm vs. 15.57 ± 1.40 µm) was significantly increased, as opposed to the outer nuclear layer (ONL) thickness, which was decreased (77.94 ± 18.30 µm vs. 88.64 ± 7.65 µm) in the vitelliform group compared to the control group. We found a continuous external limiting membrane (ELM) in 55.5% of the eyes compared to a continuous ellipsoid zone (EZ) in 22.2% of the eyes in the vitelliform group. The difference between the mean AVLs' volume at baseline compared to the last visit for the nine eyes with ophthalmologic follow-up was not statistically significant ( p = 0.725). The median follow-up duration was 11 months (range 5-56 months). Seven eyes (43.75%) were treated with intravitreal anti-vascular endothelium growth factor (anti-VEGF) agent injections, in which we noted a 6.43 ± 9 letter decrease in the best-corrected visual acuity (BCVA). The increased RPE thickness could suggest hyperplasia contrary to the decreased ONL, which could mirror the impact of the vitelliform lesion on photoreceptors (PR). Eyes that received anti-VEGF injections did not show signs of improvement regarding BCVA.

Residency training in medicine lays the foundation for future medical doctors. In real-world settings, training centers face challenges in trying to create balanced residency programs, with cases encountered by residents not always being fairly distributed among them. In recent years, there has been a tremendous advancement in developing artificial intelligence (AI)-based algorithms with human expert guidance for medical imaging segmentation, classification, and prediction. In this paper, we turned our attention from training machines to letting them train us and developed an AI framework for personalised case-based ophthalmology residency training. The framework is built on two components: (1) a deep learning (DL) model and (2) an expert-system-powered case allocation algorithm. The DL model is trained on publicly available datasets by means of contrastive learning and can classify retinal diseases from color fundus photographs (CFPs). Patients visiting the retina clinic will have a CFP performed and afterward, the image will be interpreted by the DL model, which will give a presumptive diagnosis. This diagnosis is then passed to a case allocation algorithm which selects the resident who would most benefit from the specific case, based on their case history and performance. At the end of each case, the attending expert physician assesses the resident's performance based on standardised examination files, and the results are immediately updated in their portfolio. Our approach provides a structure for future precision medical education in ophthalmology.

Aim: To evaluate the visual outcome of penetrating ocular injuries with a retained intraocular foreign body (IOFB) managed by pars plana vitrectomy (PPV) and to describe the risk factors associated with poor visual acuity and retinal detachment (RD) development. Methods: Medical records of 56 patients with IOFB that were removed by PPV over a period of 11 years (1 January 2010−31 December 2020) were reviewed. We extracted the demographic data, initial and final best corrected visual acuity (BCVA) using standard Snellen chart, IOFB characteristics, complications and surgical details. Outcome was evaluated according to the final BCVA: poor <0.1, good 0.1−<0.5 or excellent ≥0.5. Results: The mean age was 36.1 ± 14.1 (range, 16−71) years and the majority of patients were males (55 out of 56, 98.2%). IOFB was retinal in 27 (48.2%) cases and intravitreal in 29 cases (51.8%). IOFB size was ≤3mm in 26 (46.4%) cases and >3mm in 30 (53.6%) cases. Preoperative RD was identified in 12 (21.4%) cases and endophthalmitis in 17 cases (30.4%). IOFBs larger than 3 mm and retinal location were associated with RD development. Poor visual outcome was associated with initial BCVA, retinal location, RD and endophthalmitis. Conclusion: Initial BCVA, retinal foreign body, RD and endophthalmitis were risk factors for poor visual outcome.