RobOCTNet: Robotics and Deep Learning for Referable Posterior Segment Pathology Detection in an Emergency Department Population.

Purpose: To evaluate the diagnostic performance of a robotically aligned optical coherence tomography (RAOCT) system coupled with a deep learning model in detecting referable posterior segment pathology in OCT images of emergency department patients. Methods: A deep learning model, RobOCTNet, was trained and internally tested to classify OCT images as referable versus non-referable for ophthalmology consultation. For external testing, emergency department patients with signs or symptoms warranting evaluation of the posterior segment were imaged with RAOCT. RobOCTNet was used to classify the images. Model performance was evaluated against a reference standard based on clinical diagnosis and retina specialist OCT review. Results: We included 90,250 OCT images for training and 1489 images for internal testing. RobOCTNet achieved an area under the curve (AUC) of 1.00 (95% confidence interval [CI], 0.99-1.00) for detection of referable posterior segment pathology in the internal test set. For external testing, RAOCT was used to image 72 eyes of 38 emergency department patients. In this set, RobOCTNet had an AUC of 0.91 (95% CI, 0.82-0.97), a sensitivity of 95% (95% CI, 87%-100%), and a specificity of 76% (95% CI, 62%-91%). The model's performance was comparable to two human experts' performance. Conclusions: A robotically aligned OCT coupled with a deep learning model demonstrated high diagnostic performance in detecting referable posterior segment pathology in a cohort of emergency department patients. Translational Relevance: Robotically aligned OCT coupled with a deep learning model may have the potential to improve emergency department patient triage for ophthalmology referral.

Robotic Optical Coherence Tomography Retinal Imaging for Emergency Department Patients: A Pilot Study for Emergency Physicians' Diagnostic Performance.

STUDY OBJECTIVE: To evaluate the diagnostic performance of emergency physicians' interpretation of robotically acquired retinal optical coherence tomography images for detecting posterior eye abnormalities in patients seen in the emergency department (ED). METHODS: Adult patients presenting to Duke University Hospital emergency department from November 2020 through October 2021 with acute visual changes, headache, or focal neurologic deficit(s) who received an ophthalmology consultation were enrolled in this pilot study. Emergency physicians provided standard clinical care, including direct ophthalmoscopy, at their discretion. Retinal optical coherence tomography images of these patients were obtained with a robotic, semi-autonomous optical coherence tomography system. We compared the detection of abnormalities in optical coherence tomography images by emergency physicians with a reference standard, a combination of ophthalmology consultation diagnosis and retina specialist optical coherence tomography review. RESULTS: Nine emergency physicians reviewed the optical coherence tomography images of 72 eyes from 38 patients. Based on the reference standard, 33 (46%) eyes were normal, 16 (22%) had at least 1 urgent/emergency abnormality, and the remaining 23 (32%) had at least 1 nonurgent abnormality. Emergency physicians' optical coherence tomography interpretation had 69% (95% confidence interval [CI], 49% to 89%) sensitivity for any abnormality, 100% (95% CI, 79% to 100%) sensitivity for urgent/emergency abnormalities, 48% (95% CI, 28% to 68%) sensitivity for nonurgent abnormalities, and 64% (95% CI, 44% to 84%) overall specificity. In contrast, emergency physicians providing standard clinical care did not detect any abnormality with direct ophthalmoscopy. CONCLUSION: Robotic, semi-autonomous optical coherence tomography enabled ocular imaging of emergency department patients with a broad range of posterior eye abnormalities. In addition, emergency provider optical coherence tomography interpretation was more sensitive than direct ophthalmoscopy for any abnormalities, urgent/emergency abnormalities, and nonurgent abnormalities in this pilot study with a small sample of patients and emergency physicians.

Characterization of Errors in Retinopathy of Prematurity Diagnosis by Ophthalmologists-in-Training in the United States and Canada.

PURPOSE: To identify the prominent factors that lead to misdiagnosis of retinopathy of prematurity (ROP) by ophthalmologists-in-training in the United States and Canada. METHODS: This prospective cohort study included 32 ophthalmologists-in-training at six ophthalmology training programs in the United States and Canada. Twenty web-based cases of ROP using wide-field retinal images were presented, and ophthalmologists-in-training were asked to diagnose plus disease, zone, stage, and category for each eye. Responses were compared to a consensus reference standard diagnosis for accuracy, which was established by combining the clinical diagnosis and the image-based diagnosis by multiple experts. The types of diagnostic errors that occurred were analyzed with descriptive and chi-squared analysis. Main outcome measures were frequency of types (category, zone, stage, plus disease) of diagnostic errors; association of errors in zone, stage, and plus disease diagnosis with incorrectly identified category; and performance of ophthalmologists-in-training across postgraduate years. RESULTS: Category of ROP was misdiagnosed at a rate of 48%. Errors in classification of plus disease were most commonly associated with misdiagnosis of treatment-requiring (plus error rate = 16% when treatment-requiring was correctly diagnosed vs 81% when underdiagnosed as type 2 or pre-plus; mean difference: 64.3; 95% CI: 51.9 to 76.7; P < .001) and type 2 or pre-plus (plus error rate = 35% when type 2 or pre-plus was correctly diagnosed vs 76% when overdiagnosed as treatment-requiring; mean difference: 41.0; 95% CI: 28.4 to 53.5; P < .001) disease. The diagnostic error rate of postgraduate year (PGY)-2 trainees was significantly higher than PGY-3 trainees (PGY-2 category error rate = 61% vs PGY-3 = 35%; mean difference, 25.4; 95% CI: 17.7 to 33.0; P < .001). CONCLUSIONS: Ophthalmologists-in-training in the United States and Canada misdiagnosed ROP nearly half of the time, with incorrect identification of plus disease as a leading cause. Integration of structured learning for ROP in residency education may improve diagnostic competency. [J Pediatr Ophthalmol Strabismus. 2023;60(5):337-343.].

Evaluation of an Artificial Intelligence System for Retinopathy of Prematurity Screening in Nepal and Mongolia.

Purpose: To evaluate the performance of a deep learning (DL) algorithm for retinopathy of prematurity (ROP) screening in Nepal and Mongolia. Design: Retrospective analysis of prospectively collected clinical data. Participants: Clinical information and fundus images were obtained from infants in 2 ROP screening programs in Nepal and Mongolia. Methods: Fundus images were obtained using the Forus 3nethra neo (Forus Health) in Nepal and the RetCam Portable (Natus Medical, Inc.) in Mongolia. The overall severity of ROP was determined from the medical record using the International Classification of ROP (ICROP). The presence of plus disease was determined independently in each image using a reference standard diagnosis. The Imaging and Informatics for ROP (i-ROP) DL algorithm was trained on images from the RetCam to classify plus disease and to assign a vascular severity score (VSS) from 1 through 9. Main Outcome Measures: Area under the receiver operating characteristic curve and area under the precision-recall curve for the presence of plus disease or type 1 ROP and association between VSS and ICROP disease category. Results: The prevalence of type 1 ROP was found to be higher in Mongolia (14.0%) than in Nepal (2.2%; P < 0.001) in these data sets. In Mongolia (RetCam images), the area under the receiver operating characteristic curve for examination-level plus disease detection was 0.968, and the area under the precision-recall curve was 0.823. In Nepal (Forus images), these values were 0.999 and 0.993, respectively. The ROP VSS was associated with ICROP classification in both datasets (P < 0.001). At the population level, the median VSS was found to be higher in Mongolia (2.7; interquartile range [IQR], 1.3-5.4]) as compared with Nepal (1.9; IQR, 1.2-3.4; P < 0.001). Conclusions: These data provide preliminary evidence of the effectiveness of the i-ROP DL algorithm for ROP screening in neonatal populations in Nepal and Mongolia using multiple camera systems and are useful for consideration in future clinical implementation of artificial intelligence-based ROP screening in low- and middle-income countries.

External Validation of a Retinopathy of Prematurity Screening Model Using Artificial Intelligence in 3 Low- and Middle-Income Populations.

Importance: Retinopathy of prematurity (ROP) is a leading cause of preventable blindness that disproportionately affects children born in low- and middle-income countries (LMICs). In-person and telemedical screening examinations can reduce this risk but are challenging to implement in LMICs owing to the multitude of at-risk infants and lack of trained ophthalmologists. Objective: To implement an ROP risk model using retinal images from a single baseline examination to identify infants who will develop treatment-requiring (TR)-ROP in LMIC telemedicine programs. Design, Setting, and Participants: In this diagnostic study conducted from February 1, 2019, to June 30, 2021, retinal fundus images were collected from infants as part of an Indian ROP telemedicine screening program. An artificial intelligence (AI)-derived vascular severity score (VSS) was obtained from images from the first examination after 30 weeks' postmenstrual age. Using 5-fold cross-validation, logistic regression models were trained on 2 variables (gestational age and VSS) for prediction of TR-ROP. The model was externally validated on test data sets from India, Nepal, and Mongolia. Data were analyzed from October 20, 2021, to April 20, 2022. Main Outcomes and Measures: Primary outcome measures included sensitivity, specificity, positive predictive value, and negative predictive value for predictions of future occurrences of TR-ROP; the number of weeks before clinical diagnosis when a prediction was made; and the potential reduction in number of examinations required. Results: A total of 3760 infants (median [IQR] postmenstrual age, 37 [5] weeks; 1950 male infants [51.9%]) were included in the study. The diagnostic model had a sensitivity and specificity, respectively, for each of the data sets as follows: India, 100.0% (95% CI, 87.2%-100.0%) and 63.3% (95% CI, 59.7%-66.8%); Nepal, 100.0% (95% CI, 54.1%-100.0%) and 77.8% (95% CI, 72.9%-82.2%); and Mongolia, 100.0% (95% CI, 93.3%-100.0%) and 45.8% (95% CI, 39.7%-52.1%). With the AI model, infants with TR-ROP were identified a median (IQR) of 2.0 (0-11) weeks before TR-ROP diagnosis in India, 0.5 (0-2.0) weeks before TR-ROP diagnosis in Nepal, and 0 (0-5.0) weeks before TR-ROP diagnosis in Mongolia. If low-risk infants were never screened again, the population could be effectively screened with 45.0% (India, 664/1476), 38.4% (Nepal, 151/393), and 51.3% (Mongolia, 266/519) fewer examinations required. Conclusions and Relevance: Results of this diagnostic study suggest that there were 2 advantages to implementation of this risk model: (1) the number of examinations for low-risk infants could be reduced without missing cases of TR-ROP, and (2) high-risk infants could be identified and closely monitored before development of TR-ROP.

Variability in Plus Disease Diagnosis using Single and Serial Images.

PURPOSE: To assess changes in retinopathy of prematurity (ROP) diagnosis in single and serial retinal images. DESIGN: Cohort study. PARTICIPANTS: Cases of ROP recruited from the Imaging and Informatics in Retinopathy of Prematurity (i-ROP) consortium evaluated by 7 graders. METHODS: Seven ophthalmologists reviewed both single and 3 consecutive serial retinal images from 15 cases with ROP, and severity was assigned as plus, preplus, or none. Imaging data were acquired during routine ROP screening from 2011 to 2015, and a reference standard diagnosis was established for each image. A secondary analysis was performed using the i-ROP deep learning system to assign a vascular severity score (VSS) to each image, ranging from 1 to 9, with 9 being the most severe disease. This score has been previously demonstrated to correlate with the International Classification of ROP. Mean plus disease severity was calculated by averaging 14 labels per image in serial and single images to decrease noise. MAIN OUTCOME MEASURES: Grading severity of ROP as defined by plus, preplus, or no ROP. RESULTS: Assessment of serial retinal images changed the grading severity for > 50% of the graders, although there was wide variability. Cohen's kappa ranged from 0.29 to 1.0, which showed a wide range of agreement from slight to perfect by each grader. Changes in the grading of serial retinal images were noted more commonly in cases of preplus disease. The mean severity in cases with a diagnosis of plus disease and no disease did not change between single and serial images. The ROP VSS demonstrated good correlation with the range of expert classifications of plus disease and overall agreement with the mode class (P = 0.001). The VSS correlated with mean plus disease severity by expert diagnosis (correlation coefficient, 0.89). The more aggressive graders tended to be influenced by serial images to increase the severity of their grading. The VSS also demonstrated agreement with disease progression across serial images, which progressed to preplus and plus disease. CONCLUSIONS: Clinicians demonstrated variability in ROP diagnosis when presented with both single and serial images. The use of deep learning as a quantitative assessment of plus disease has the potential to standardize ROP diagnosis and treatment.

POSTERIOR TRACTIONAL MEMBRANES FOLLOWING ANTI-VASCULAR ENDOTHELIAL GROWTH FACTOR FOR RETINOPATHY OF PREMATURITY.

PURPOSE: To report two cases of tractional membrane formation following treatment with anti-vascular endothelial growth factor therapy in infants with Stage-3 retinopathy of prematurity. METHODS: Retrospective review of electronic medical record for historical information, clinical examination documentation, and imaging from fundus photography, retinal ultrasonography, and fluorescein angiography. RESULTS: Two patients with Stage-3 retinopathy of prematurity, previously treated with laser therapy and intravitreal bevacizumab, were referred to our institution for tractional membranes. The first case is of a male infant with Zone-II disease that progressed to Stage 4A with evidence of inferotemporal tractional retinal detachment only in the left eye. The second case is of a male infant with stable Zone-I disease with an epiretinal membrane in the left eye.Pars plicata vitrectomy and membranectomy were required for both cases because of the concern for subsequent tractional retinal detachment. CONCLUSION: Formation of tractional retinal membranes has been associated with anti-vascular endothelial growth factor therapy. These cases describe the formation of posterior tractional membranes after anti-vascular endothelial growth factor therapy. This potential ocular outcome should be considered when determining treatment plans for retinopathy of prematurity.

Description and management of retinopathy of prematurity reactivation after intravitreal antivascular endothelial growth factor therapy.

PURPOSE OF REVIEW: To review the literature regarding reactivation of retinopathy of prematurity (ROP) after treatment with antivascular endothelial growth factor (anti-VEGF) agents. RECENT FINDINGS: Reactivation can occur after anti-VEGF or laser. Risk factors for reactivation include patient and disease-related factors. Various studies are evaluating the use of different anti-VEGF agents and reactivation rates. However, the definition of reactivation varies between studies. SUMMARY: The literature has varied definitions of reactivation, which is often used interchangeably with recurrence. It is important to recognize features of reactivation of ROP to appropriately manage patients and conduct clinical trials. The International Classification of ROP 3rd edition has established a consensus guideline regarding terminology describing reactivation.

Addressing the Third Epidemic of Retinopathy of Prematurity Through Telemedicine and Technology: A Systematic Review.

The rising prevalence of retinopathy of prematurity (ROP) in low- and middle-income countries has increased the need for screening at-risk infants. The purpose of this article was to review the impact of tele-medicine and technology on ROP screening programs. Adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic review was performed using PubMed, Pro-Quest, and Google Scholar bibliographic search engine. Terms searched included retinopathy of prematurity, telemedicine, and tele-ophthalmology. Data regarding internet access and gross domestic product per capita were obtained from the World Bank. Information was also obtained about internet access, speeds, and costs in low-income countries. There has been increasing integration of telemedicine and technology for ROP screening and management. Low-income countries are using available internet options and information and communications technology for ROP screening, which can aid in addressing the unique challenges faced by low-income countries. This provides a promising solution to the third epidemic of ROP by expanding and improving screening and management. Although telemedicine systems may serve as a cost-effective approach to facilitate delivery of health care, programs (especially in lowand middle-income countries) require national support to maintain its infrastructure. [J Pediatr Ophthalmol Strabismus. 2021;58(4):261-269.].

Impact of Artificial Intelligence on Medical Education in Ophthalmology.

Clinical care in ophthalmology is rapidly evolving as artificial intelligence (AI) algorithms are being developed. The medical community and national and federal regulatory bodies are recognizing the importance of adapting to AI. However, there is a gap in physicians' understanding of AI and its implications regarding its potential use in clinical care, and there are limited resources and established programs focused on AI and medical education in ophthalmology. Physicians are essential in the application of AI in a clinical context. An AI curriculum in ophthalmology can help provide physicians with a fund of knowledge and skills to integrate AI into their practice. In this paper, we provide general recommendations for an AI curriculum for medical students, residents, and fellows in ophthalmology.

Evaluation of pediatric ophthalmologists' perspectives of artificial intelligence in ophthalmology.

PURPOSE: To survey pediatric ophthalmologists on their perspectives of artificial intelligence (AI) in ophthalmology. METHODS: This is a subgroup analysis of a study previously reported. In March 2019, members of the American Association for Pediatric Ophthalmology and Strabismus (AAPOS) were recruited via the online AAPOS discussion board to voluntarily complete a Web-based survey consisting of 15 items. Survey items assessed the extent participants "agreed" or "disagreed" with statements on the perceived benefits and concerns of AI in ophthalmology. Responses were analyzed using descriptive statistics. RESULTS: A total of 80 pediatric ophthalmologists who are members of AAPOS completed the survey. The mean number of years since graduating residency was 21 years (range, 0-46). Overall, 91% (73/80) reported understanding the concept of AI, 70% (56/80) believed AI will improve the practice of ophthalmology, 68% (54/80) reported willingness to incorporate AI into their clinical practice, 65% (52/80) did not believe AI will replace physicians, and 71% (57/80) believed AI should be incorporated into medical school and residency curricula. However, 15% (12/80) were concerned that AI will replace physicians, 26% (21/80) believed AI will harm the patient-physician relationship, and 46% (37/80) reported concern over the diagnostic accuracy of AI. CONCLUSIONS: Most pediatric ophthalmologists in this survey viewed the role of AI in ophthalmology positively.

Development of Screening Criteria for Retinopathy of Prematurity in Ulaanbaatar, Mongolia, Using a Web-based Data Management System.

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; 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.].

Evaluation of Potential Systemic Adverse Events Related to Fluorescein Angiography in Pediatric Patients.

PURPOSE: To evaluate adverse events of fluorescein angiography (FA) in pediatric patients. DESIGN: Single-institution retrospective chart review. PARTICIPANTS: Patients 0 to 18 years of age who underwent FA between January 2010 and December 2015 at a single institution in the United States. METHODS: Pediatric patients who underwent FA by 3 surgeons were included in the study. Patients with fewer than 24 hours of documented follow-up were excluded. Significant adverse events within 24 hours of FA were evaluated. Detailed intraoperative and perioperative physiological parameters, including heart rate, blood pressure, oxygen saturation, and ventilation parameters, in inpatients undergoing simultaneous examination under anesthesia were reviewed. Peri-injection effects of FA were evaluated by 2-tailed paired t test comparison of mean 5-minute preinjection and 5-minute postinjection physiological data. MAIN OUTCOME MEASURES: Significant adverse events associated with FA. RESULTS: One hundred fifteen patients with a total of 214 FA examinations were included. No significant adverse events were associated directly with FA. Comparison of mean 5-minute preinjection and postinjection physiologic parameters in 27 patients who underwent intravenous FA during EUA did not reveal significant changes associated with FA. A significant difference was found in average patient age between inpatient (2.5 years) and outpatient (10.7 years) FA (P < 0.00001). The youngest patients who underwent successful FA were 3.8 years old in the outpatient setting and 32 weeks' postmenstrual age in the inpatient setting. Patients younger than 3.8 years accounted for most (77.6%; n = 85) inpatient FA examinations. Excluding patients with a need or likely need for laser or surgery, the reasons for inpatient FA in patients older than 3.8 years included the lack of availability of outpatient ultra-widefield FA (UWFA) and more challenging situations in patients with developmental delay. CONCLUSIONS: Fluorescein angiography was not found to be associated directly with systemic adverse events in pediatric patients in this study. Younger patients more commonly were found to require an inpatient FA, whereas older patients older than 4 years underwent outpatient UWFA.

Ocular features of Townes-Brocks syndrome.

Townes-Brocks syndrome (TBS) is a rare genetic syndrome associated with heterozygous mutations in SALL1 and characterized by abnormalities of the anus, ear, and thumb. Ophthalmic findings have been rarely reported and include congenital cataract, microphthalmia, optic nerve atrophy, coloboma, epibulbar dermoid, and dysinnervation patterns, such as Duane syndrome and gustatory lacrimation. We report a case of genetically confirmed TBS showing a spectrum of ocular anomalous innervations, including bilateral type 1 Duane syndrome and Möbius sequence, left-sided Marcus Gunn jaw winking, left eye gustatory lacrimation, and lack of emotional tearing bilaterally. Magnetic resonance imaging of brain and orbit showed absence of the abducens nerve bilaterally and of the left facial nerve.

Eye Symptom Questionnaire to Evaluate Anterior Eye Health.

OBJECTIVES: Ophthalmologists assess eye complaints with a careful history and eye examination; however, other types of physicians have limited tools to evaluate anterior segment (AS) eye diseases. We identified the eye symptom questions that providers should ask to help determine the presence and urgency of AS eye diseases. METHODS: Persons with and without AS disease completed a self-report eye symptom questionnaire (ESQ) based on the National Institutes of Health Toolbox symptom items in an academic center's corneal and comprehensive eye clinics. Gold standard ophthalmic examination determined the presence and urgency of AS disease. The association between reported symptom severity and the probability of AS disease, or urgent AS disease, was evaluated using logistic regression models, and sensitivity and specificity of the ESQ were also calculated. RESULTS: A total of 324 eyes of 162 subjects were included in the study. Of these, AS disease was present in 255 eyes (79%); of which, 111 eyes showed urgent disease. Increasing symptom severity for eye pain (odds ratio [OR]=2.58; P<0.001), glare (OR=2.61; P=0.001), and blurry vision (OR=1.98; P<0.001) were associated with increased odds of AS disease. Increasing symptom severity for eye pain (OR=2.02; P<0.001), eye redness (OR=1.69; P=0.02), and blurry vision (OR=1.41, P=0.01) were associated with increased odds of urgent AS disease. For the primary analysis with mild symptoms considered relevant, the sensitivity of the ESQ to detect AS disease was 83% and to detect urgent AS disease was 92%. CONCLUSION: Symptoms of eye pain, glare, redness, and blurry vision indicate the presence and urgency of AS disease.

Novel Image-Based Analysis for Reduction of Clinician-Dependent Variability in Measurement of the Corneal Ulcer Size.

PURPOSE: To assess variability in corneal ulcer measurements between ophthalmologists and reduce clinician-dependent variability using semiautomated segmentation of the ulcer from photographs. METHODS: Three ophthalmologists measured 50 patients' eyes for epithelial defects (EDs) and the stromal infiltrate (SI) size using slit-lamp (SL) calipers. SL photographs were obtained. An algorithm was developed for semiautomatic segmenting of the ED and SI in the photographs. Semiautomatic segmentation was repeated 3 times by different users (2 ophthalmologists and 1 trainee). Clinically significant variability was assessed with intraclass correlation coefficients (ICCs) and the percentage of pairwise measurements differing by ≥0.5 mm. Semiautomatic segmentation measurements were compared with manual delineation of the image by a corneal specialist (gold standard) using Dice similarity coefficients. RESULTS: Ophthalmologists' reliability in measurements by SL calipers had an ICC from 0.84 to 0.88 between examiners. Measurements by semiautomatic segmentation had an ICC from 0.96 to 0.98. SL measures of ulcers by clinical versus semiautomatic segmentation measures differed by ≥0.5 mm in 24% to 38% versus 8% to 28% (ED height); 30% to 52% versus 12% to 34% (ED width); 26% to 38% versus 10% to 32% (SI height); and 38% to 58% versus 14% to 34% (SI width), respectively. Average Dice similarity coefficients between manual and repeated semiautomatic segmentation ranged from 0.83 to 0.86 for the ED and 0.78 to 0.83 for the SI. CONCLUSIONS: Variability exists when measuring corneal ulcers, even among ophthalmologists. Photography and computerized methods for quantifying the ulcer size could reduce variability while remaining accurate and impact quantitative measurement endpoints.