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IMPORTANCE: Retinopathy of prematurity (ROP) is a leading cause of blindness in children, with significant disparities in outcomes between high-income and low-income countries, due in part to insufficient access to ROP screening. OBJECTIVE: To evaluate how well autonomous artificial intelligence (AI)–based ROP screening can detect more-than-mild ROP (mtmROP) and type 1 ROP. DESIGN, SETTING, AND PARTICIPANTS: This diagnostic study evaluated the performance of an AI algorithm, trained and calibrated using 2530 examinations from 843 infants in the Imaging and Informatics in Retinopathy of Prematurity (i-ROP) study, on 2 external datasets (6245 examinations from 1545 infants in the Stanford University Network for Diagnosis of ROP [SUNDROP] and 5635 examinations from 2699 infants in the Aravind Eye Care Systems [AECS] telemedicine programs). Data were taken from 11 and 48 neonatal care units in the US and India, respectively. Data were collected from January 2012 to July 2021, and data were analyzed from July to December 2023. EXPOSURES: An imaging processing pipeline was created using deep learning to autonomously identify mtmROP and type 1 ROP in eye examinations performed via telemedicine. MAIN OUTCOMES AND MEASURES: The area under the receiver operating characteristics curve (AUROC) as well as sensitivity and specificity for detection of mtmROP and type 1 ROP at the eye examination and patient levels. RESULTS: The prevalence of mtmROP and type 1 ROP were 5.9% (91 of 1545) and 1.2% (18 of 1545), respectively, in the SUNDROP dataset and 6.2% (168 of 2699) and 2.5% (68 of 2699) in the AECS dataset. Examination-level AUROCs for mtmROP and type 1 ROP were 0.896 and 0.985, respectively, in the SUNDROP dataset and 0.920 and 0.982 in the AECS dataset. At the cross-sectional examination level, mtmROP detection had high sensitivity (SUNDROP: mtmROP, 83.5%; 95% CI, 76.6-87.7; type 1 ROP, 82.2%; 95% CI, 81.2-83.1; AECS: mtmROP, 80.8%; 95% CI, 76.2-84.9; type 1 ROP, 87.8%; 95% CI, 86.8-88.7). At the patient level, all infants who developed type 1 ROP screened positive (SUNDROP: 100%; 95% CI, 81.4-100; AECS: 100%; 95% CI, 94.7-100) prior to diagnosis. CONCLUSIONS AND RELEVANCE: Where and when ROP telemedicine programs can be implemented, autonomous ROP screening may be an effective force multiplier for secondary prevention of ROP.

IMPORTANCE: Retinopathy of prematurity (ROP) telemedicine screening programs have been found to be effective, but they rely on widefield digital fundus imaging (WDFI) cameras, which are expensive, making them less accessible in low- to middle-income countries. Cheaper, smartphone-based fundus imaging (SBFI) systems have been described, but these have a narrower field of view (FOV) and have not been tested in a real-world, operational telemedicine setting. OBJECTIVE: To assess the efficacy of SBFI systems compared with WDFI when used by technicians for ROP screening with both artificial intelligence (AI) and human graders. DESIGN, SETTING, AND PARTICIPANTS: This prospective cross-sectional comparison study took place as a single-center ROP teleophthalmology program in India from January 2021 to April 2022. Premature infants who met normal ROP screening criteria and enrolled in the teleophthalmology screening program were included. Those who had already been treated for ROP were excluded. EXPOSURES: All participants had WDFI images and from 1 of 2 SBFI devices, the Make-In-India (MII) Retcam or Keeler Monocular Indirect Ophthalmoscope (MIO) devices. Two masked readers evaluated zone, stage, plus, and vascular severity scores (VSS, from 1-9) in all images. Smartphone images were then stratified by patient into training (70%), validation (10%), and test (20%) data sets and used to train a ResNet18 deep learning architecture for binary classification of normal vs preplus or plus disease, which was then used for patient-level predictions of referral warranted (RW)– and treatment requiring (TR)–ROP. MAIN OUTCOME AND MEASURES: Sensitivity and specificity of detection of RW-ROP, and TR-ROP by both human graders and an AI system and area under the receiver operating characteristic curve (AUC) of grader-assigned VSS. Sensitivity and specificity were compared between the 2 SBFI systems using Pearson χ2testing. RESULTS: A total of 156 infants (312 eyes; mean [SD] gestational age, 33.0 [3.0] weeks; 75 [48%] female) were included with paired examinations. Sensitivity and specificity were not found to be statistically different between the 2 SBFI systems. Human graders were effective with SBFI at detecting TR-ROP with a sensitivity of 100% and specificity of 83.49%. The AUCs with grader-assigned VSS only were 0.95 (95% CI, 0.91-0.99) and 0.96 (95% CI, 0.93-0.99) for RW-ROP and TR-ROP, respectively. For the AI system, the sensitivity of detecting TR-ROP sensitivity was 100% with specificity of 58.6%, and RW-ROP sensitivity was 80.0% with specificity of 59.3%. CONCLUSIONS AND RELEVANCE: In this cross-sectional study, 2 different SBFI systems used by technicians in an ROP screening program were highly sensitive for TR-ROP. SBFI systems with AI may be a cost-effective method to improve the global capacity for ROP screening.

The authors report a multicenter pilot study examining the use of 27-G vitrectomy and instrumentation in pediatric patients, ranging from premature infants with retinopathy of prematurity, to older children with traumatic macular hole and endophthalmitis.

This is the first article to report the outcomes of combined glaucoma, retina, and corneal transplantation surgery in pediatric patients. It will aid the cornea specialist in counseling families when considering treatment options for complex eye diseases.

To compare the performance of deep learning classifiers for the diagnosis of plus disease in retinopathy of prematurity (ROP) trained using 2 methods for developing models on multi-institutional data sets: centralizing data versus federated learning (FL) in which no data leave each institution.

Retinopathy of prematurity is a leading cause of childhood blindness worldwide, but clinical diagnosis is subjective, which leads to treatment differences. Our goal was to determine objective differences in the diagnosis of plus disease between clinicians using an automated retinopathy of prematurity (ROP) vascular severity score.

Multispectral imaging (MSI) of the retina and choroid has increasing interest for better diagnosis and treatment evaluation of eye diseases. However, currently available MSI systems have a limited field of view (FOV) to evaluate the peripheral retina. This study is to validate trans-pars-planar illumination for a contact-mode ultra-widefield MSI system. By freeing the available pupil for collecting imaging light only, the trans-pars-planar illumination enables a portable, non-mydriatic fundus camera, with 200° FOV in a single fundus image. The trans-pars-planar illumination, delivering illumination light from one side of the eye, naturally enables oblique illumination ophthalmoscopy to enhance the contrast of fundus imaging. A broadband (104 nm) 565 nm light-emitting diode (LED) is used for validating color fundus imaging first. Four narrowband (17-60 nm) 530 nm, 625 nm, 780 nm, and 970 nm LEDs are tested for MSI. With 530 nm illumination, the fundus image reveals retinal vasculature predominantly. 625 nm and 780 nm illuminations enhance the visibility of choroidal vasculature. With further increased wavelength of 970 nm, the fundus image is predominated by large veins in the choroid, with multiple vortex ampullas observed simultaneously in a single fundus image.

Purpose:To describe a process for identifying birth weight (BW) and gestational age (GA) screening guidelines in Mongolia.Methods:This was a prospective cohort study in a tertiary care hospital in Ulaanbataar, Mongolia, of 193 premature infants with GA of 36 weeks or younger and/or BW of 2,000 g or less) with regression analysis to determine associations between BW and GA and the development of retinopathy of prematurity (ROP).Results:As BW and GA decreased, the relative risk of developing ROP increased. The relative risk of developing any stage of ROP in infants born at 29 weeks or younger was 2.91 (95% CI: 1.55 to 5.44; As BW and GA decreased, the relative risk of developing ROP increased. The relative risk of developing any stage of ROP in infants born at 29 weeks or younger was 2.91 (95% CI: 1.55 to 5.44; P< .001] compared to older infants. The relative risk of developing any type of ROP in infants with BW of less than 1,200 g was 2.41 (95% CI: 1.35 to 4.29; P= .003] and developing type 2 or worse ROP was 2.05 (95% CI: 0.99 to 4.25; P= .05).Conclusions:Infants in Mongolia with heavier BW and older GA who fall outside of current United States screening guidelines of GA of 30 weeks or younger and/or BW of 1,500 g or less developed clinically relevant ROP.[[J Pediatr Ophthalmol Strabismus. 2020;57(5):333–339.]

To evaluate adverse events of fluorescein angiography (FA) in pediatric patients.

This study is to test the feasibility of using trans-pars-planar illumination for ultra-wide field pediatric fundus photography. Fundus examination of the peripheral retina is essential for clinical management of pediatric eye diseases. However, current pediatric fundus cameras with traditional trans-pupillary illumination provide a limited field of view (FOV), making it difficult to access the peripheral retina adequately for a comprehensive assessment of eye conditions. Here, we report the first demonstration of trans-pars-planar illumination in ultra-wide field pediatric fundus photography. For proof-of-concept validation, all off-the-shelf optical components were selected to construct a lab prototype pediatric camera (PedCam). By freeing the entire pupil for imaging purpose only, the trans-pars-planar illumination enables a 200° FOV PedCam, allowing easy visualization of both the central and peripheral retina up to the ora serrata. A low-cost, easy-to-use ultra-wide field PedCam provides a unique opportunity to foster affordable telemedicine in rural and underserved areas.

BackgroundTo demonstrate a technique for using optical coherence tomography angiography (OCTA) to simulate leakage in eyes with diabetic macular oedema and determine the sensitivity and positive predictive value of detecting leaking microvasculature on OCTA using fluorescein angiography (FA) as the comparative norm.Methods6×6 mm OCT angiograms were overlaid with the corresponding OCT thickness maps. Microvascular abnormalities on the OCT angiogram underlying areas of thickening on the OCT thickness map were assumed to be leaking. Two independent readers blindly read the OCTA overlay images then the FA images cropped to the same approximate region to delineate areas of leaking microvasculature. The results were compared to determine the sensitivity and positive predictive value of OCTA for detection of leaking vessels.Results28 eyes of 19 diabetic patients were included. Each eye demonstrated an average of seven leaking microvascular abnormalities on the OCTA images and 22 leaking abnormalities on the FA images. Sensitivity of leaking microvasculature detection by OCTA was 26.1% and positive predictive value was 68.4%. The correlation coefficient of the two readers’ detection of leaking microvasculature was 0.605 for OCTA reads compared with 0.916 for FA.ConclusionOCTA as a whole can be used to simulate leakage, but currently, sensitivity of the technique is low. Further understanding of the OCTA technology may yield novel means of detecting retinal pathology.

IMPORTANCE: Retinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide, but clinical diagnosis is subjective and qualitative. OBJECTIVE: To describe a quantitative ROP severity score derived using a deep learning algorithm designed to evaluate plus disease and to assess its utility for objectively monitoring ROP progression. DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study included images from 5255 clinical examinations of 871 premature infants who met the ROP screening criteria of the Imaging and Informatics in ROP (i-ROP) Consortium, which comprises 9 tertiary care centers in North America, from July 1, 2011, to December 31, 2016. Data analysis was performed from July 2017 to May 2018. EXPOSURE: A deep learning algorithm was used to assign a continuous ROP vascular severity score from 1 (most normal) to 9 (most severe) at each examination based on a single posterior photograph compared with a reference standard diagnosis (RSD) simplified into 4 categories: no ROP, mild ROP, type 2 ROP or pre-plus disease, or type 1 ROP. Disease course was assessed longitudinally across multiple examinations for all patients. MAIN OUTCOMES AND MEASURES: Mean ROP vascular severity score progression over time compared with the RSD. RESULTS: A total of 5255 clinical examinations from 871 infants (mean [SD] gestational age, 27.0 [2.0] weeks; 493 [56.6%] male; mean [SD] birth weight, 949 [271] g) were analyzed. The median severity scores for each category were as follows: 1.1 (interquartile range [IQR], 1.0-1.5) (no ROP), 1.5 (IQR, 1.1-3.4) (mild ROP), 4.6 (IQR, 2.4-5.3) (type 2 and pre-plus), and 7.5 (IQR, 5.0-8.7) (treatment-requiring ROP) (P &lt; .001). When the long-term differences in the median severity scores across time between the eyes progressing to treatment and those who did not eventually require treatment were compared, the median score was higher in the treatment group by 0.06 at 30 to 32 weeks, 0.75 at 32 to 34 weeks, 3.56 at 34 to 36 weeks, 3.71 at 36 to 38 weeks, and 3.24 at 38 to 40 weeks postmenstrual age (P &lt; .001 for all comparisons). CONCLUSIONS AND RELEVANCE: The findings suggest that the proposed ROP vascular severity score is associated with category of disease at a given point in time and clinical progression of ROP in premature infants. Automated image analysis may be used to quantify clinical disease progression and identify infants at high risk for eventually developing treatment-requiring ROP. This finding has implications for quality and delivery of ROP care and for future approaches to disease classification.

IMPORTANCE: Retinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide, but treatment failure and disease recurrence are important causes of adverse outcomes in patients with treatment-requiring ROP (TR-ROP). OBJECTIVES: To apply an automated ROP vascular severity score obtained using a deep learning algorithm and to assess its utility for objectively monitoring ROP regression after treatment. DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study used data from the Imaging and Informatics in ROP consortium, which comprises 9 tertiary referral centers in North America that screen high volumes of at-risk infants for ROP. Images of 5255 clinical eye examinations from 871 infants performed between July 2011 and December 2016 were assessed for eligibility in the present study. The disease course was assessed with time across the numerous examinations for patients with TR-ROP. Infants born prematurely meeting screening criteria for ROP who developed TR-ROP and who had images captured within 4 weeks before and after treatment as well as at the time of treatment were included. MAIN OUTCOMES AND MEASURES: The primary outcome was mean (SD) ROP vascular severity score before, at time of, and after treatment. A deep learning classifier was used to assign a continuous ROP vascular severity score, which ranged from 1 (normal) to 9 (most severe), at each examination. A secondary outcome was the difference in ROP vascular severity score among eyes treated with laser or the vascular endothelial growth factor antagonist bevacizumab. Differences between groups for both outcomes were assessed using unpaired 2-tailed t tests with Bonferroni correction. RESULTS: Of 5255 examined eyes, 91 developed TR-ROP, of which 46 eyes met the inclusion criteria based on the available images. The mean (SD) birth weight of those patients was 653 (185) g, with a mean (SD) gestational age of 24.9 (1.3) weeks. The mean (SD) ROP vascular severity scores significantly increased 2 weeks prior to treatment (4.19 [1.75]), peaked at treatment (7.43 [1.89]), and decreased for at least 2 weeks after treatment (4.00 [1.88]) (all P &lt; .001). Eyes requiring retreatment with laser had higher ROP vascular severity scores at the time of initial treatment compared with eyes receiving a single treatment (P &lt; .001). CONCLUSIONS AND RELEVANCE: This quantitative ROP vascular severity score appears to consistently reflect clinical disease progression and posttreatment regression in eyes with TR-ROP. These study results may have implications for the monitoring of patients with ROP for treatment failure and disease recurrence and for determining the appropriate level of disease severity for primary treatment in eyes with aggressive disease.

Purpose:To characterize retinopathy of prematurity (ROP) training practices in international residency and fellowship programs.Methods:A publicly available online-based platform (A publicly available online-based platform (http://www.SurveyMonkey.com) was used to develop a 28-question multiple-choice survey that targeted ROP screening and treatment methods. The authors solicited training programs in the Philippines, Thailand, and Taiwan.Results:Programs from three countries participated in the survey, and a total of 95 responses collected from residents, fellows, and attending ophthalmologists were analyzed. A descriptive analysis demonstrated that 45 participants (47%) reported 1% to 33% of ROP screenings were performed under direct supervision of attending ophthalmologists, and 35 (37%) reported the use of formal assessments. The majority of participants (Country A: 87%, Country B: 71%, and Country C: 75%) estimated 1% to 33% of their practice was spent screening for ROP. Notably, 44 participants (46%) reported performing zero laser photocoagulation treatments for ROP during training (Country A: 65%, Country B: 38%, and Country C: 38%).Conclusions:International ophthalmology trainees perform a limited number of ROP examinations and laser interventions. ROP screenings are often unsupervised and lead to no formal evaluation by an attending ophthalmologist. Limited ROP training among ophthalmologists may lead to misdiagnosis and ultimately mismanagement of a patient. Loss of vision and exposure to unwarranted treatments are among the implications of such errors. The findings highlight the need to improve ROP training in international ophthalmology residency and fellowship programs.[[J Pediatr Ophthalmol Strabismus. 2019;56(5):282–287.]

Accurate image-based ophthalmic diagnosis relies on fundus image clarity. This has important implications for the quality of ophthalmic diagnoses and for emerging methods such as telemedicine and computer-based image analysis. The purpose of this study was to implement a deep convolutional neural network (CNN) for automated assessment of fundus image quality in retinopathy of prematurity (ROP).

BACKGROUND AND OBJECTIVE:Aggressive posterior vitreoretinopathy (APVR) manifests with a broad area of retinal avascularity, progressive neovascularization, and/or tractional retinal detachment during the neonatal period.PATIENTS AND METHODS:A multicenter, retrospective, observational, consecutive case series study was performed to evaluate the retinal findings and structural retinal outcomes in patients treated for APVR within the first 3 months of life.RESULTS:Three premature neonates with a non-retinopathy of prematurity (ROP) APVR identified during routine ROP screening exams exhibited relatively severe, rapidly progressive retinal vascular abnormalities. Immediate laser photocoagulation of the avascular retina and vitrectomy for traction retinal detachment within several days to weeks improved or stabilized the retinal anatomy in all cases.CONCLUSIONS:This series describes clinical features in APVR in premature infants and suggests that early diagnosis and intervention may mitigate the typical aggressive course and poor prognosis of this condition.[[Ophthalmic Surg Lasers Imaging Retina.2019;50:201–207.]

IMPORTANCE: Retinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide. The decision to treat is primarily based on the presence of plus disease, defined as dilation and tortuosity of retinal vessels. However, clinical diagnosis of plus disease is highly subjective and variable. OBJECTIVE: To implement and validate an algorithm based on deep learning to automatically diagnose plus disease from retinal photographs. DESIGN, SETTING, AND PARTICIPANTS: A deep convolutional neural network was trained using a data set of 5511 retinal photographs. Each image was previously assigned a reference standard diagnosis (RSD) based on consensus of image grading by 3 experts and clinical diagnosis by 1 expert (ie, normal, pre–plus disease, or plus disease). The algorithm was evaluated by 5-fold cross-validation and tested on an independent set of 100 images. Images were collected from 8 academic institutions participating in the Imaging and Informatics in ROP (i-ROP) cohort study. The deep learning algorithm was tested against 8 ROP experts, each of whom had more than 10 years of clinical experience and more than 5 peer-reviewed publications about ROP. Data were collected from July 2011 to December 2016. Data were analyzed from December 2016 to September 2017. EXPOSURES: A deep learning algorithm trained on retinal photographs. MAIN OUTCOMES AND MEASURES: Receiver operating characteristic analysis was performed to evaluate performance of the algorithm against the RSD. Quadratic-weighted κ coefficients were calculated for ternary classification (ie, normal, pre–plus disease, and plus disease) to measure agreement with the RSD and 8 independent experts. RESULTS: Of the 5511 included retinal photographs, 4535 (82.3%) were graded as normal, 805 (14.6%) as pre–plus disease, and 172 (3.1%) as plus disease, based on the RSD. Mean (SD) area under the receiver operating characteristic curve statistics were 0.94 (0.01) for the diagnosis of normal (vs pre–plus disease or plus disease) and 0.98 (0.01) for the diagnosis of plus disease (vs normal or pre–plus disease). For diagnosis of plus disease in an independent test set of 100 retinal images, the algorithm achieved a sensitivity of 93% with 94% specificity. For detection of pre–plus disease or worse, the sensitivity and specificity were 100% and 94%, respectively. On the same test set, the algorithm achieved a quadratic-weighted κ coefficient of 0.92 compared with the RSD, outperforming 6 of 8 ROP experts. CONCLUSIONS AND RELEVANCE: This fully automated algorithm diagnosed plus disease in ROP with comparable or better accuracy than human experts. This has potential applications in disease detection, monitoring, and prognosis in infants at risk of ROP.