We tested the accuracy of ChatGPT, a large language model (LLM), in the ophthalmology question-answering space using two popular multiple choice question banks used for the high-stakes Ophthalmic Knowledge Assessment Program (OKAP) exam. The testing sets were of easy-to-moderate difficulty and were diversified, including recall, interpretation, practical and clinical decision-making problems. ChatGPT achieved 55.8% and 42.7% accuracy in the two 260-question simulated exams. Its performance varied across subspecialties, with the best results in general medicine and the worst in neuro-ophthalmology and ophthalmic pathology and intraocular tumors. These results are encouraging but suggest that specialising LLMs through domain-specific pre-training may be necessary to improve their performance in ophthalmic subspecialties.

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This study assessed the performance of automated machine learning (AutoML) in classifying cataract surgery phases from surgical videos. Two ophthalmology trainees without coding experience designed a deep learning model in Google Cloud AutoML Video Classification for the classification of 10 different cataract surgery phases. We used two open-access publicly available datasets (total of 122 surgeries) for model training, validation and testing. External validation was performed on 10 surgeries issued from another dataset. The AutoML model demonstrated excellent discriminating performance, even outperforming bespoke deep learning models handcrafter by experts. The area under the precision-recall curve was 0.855. At the 0.5 confidence threshold cut-off, the overall performance metrics were as follows: sensitivity (81.0%), recall (77.1%), accuracy (96.0%) and F1 score (0.79). The per-segment metrics varied across the surgical phases: precision 66.7–100%, recall 46.2–100% and specificity 94.1–100%. Hydrodissection and phacoemulsification were the most accurately predicted phases (100 and 92.31% correct predictions, respectively). During external validation, the average precision was 54.2% (0.00–90.0%), the recall was 61.1% (0.00–100%) and specificity was 96.2% (91.0–99.0%). In conclusion, a code-free AutoML model can accurately classify cataract surgery phases from videos with an accuracy comparable or better than models developed by experts.

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To systematically review the characteristics of patients with endogenous tuberculous (TB) endophthalmitis and panophthalmitis in an effort to help clinicians with diagnosis and treatment. Patients and Methods: We conducted a systematic literature search in MEDLINE/ PubMed, EMBASE and Web of Science from inception to August 2020. References and abstracts were screened independently by two authors. Included studies were case reports and case series reporting endogenous TB endophthalmitis and panophthalmitis secondary to Mycobacterium tuberculosis complex (MTBC). Available-case analysis was employed to handle missing data. Results: A total of 1343 articles were found using the search strategy. Following abstract screening, 51 articles were selected for full text-review, from which 26 were deemed eligible for inclusion in the study. Forty-four cases from 26 articles were included in the quantitative analysis. The median age of presentation was 29.5 (range: 1 to 81), and 11/44 patients (25.0%) were pediatric. Immunosuppression was seen in 9/36 cases (25.0%). Most patients (24/38, 63.2%) had no prior history of tuberculosis. Systemic symptoms were absent in half of the patients (16/32, 50.0%). Visual acuity was poor, with 23/27 cases (85.2%) being 20/ 200 or worse at presentation. Poor organ and visual outcomes were reported: 36/43 cases (83.7%) resulted in enucleation/evisceration or exenteration. Intraocular tumors were suspected in 5/34 cases (14.7%). Pulmonary tuberculosis was seen in 15/35 cases (42.8%), and miliary tuberculosis was seen in 7/35 cases (20.0%). The earliest source of TB diagnosis was through histopathologic specimen after eye removal in 32/44 cases (72.7%), vitreous specimen in 6/44 cases (13.6%) and aqueous specimen in 3/44 cases (6.8%). Conclusion: TB endophthalmitis is a rare and sight-threatening manifestation of ocular tuberculosis. It can occur in apparently healthy individuals and can mimic intraocular tumors and other infectious etiologies. Diagnosis remains a significant challenge, which, often delayed, leads to profound visual loss. Early detection and treatment of intraocular tuberculosis may be associated with better ocular and systemic outcomes.

Background To evaluate the rate and risk factors of epiretinal membrane (ERM) formation and need for ERM peeling after pars plana vitrectomy (PPV) for uncomplicated primary rhegmatogenous retinal detachment (RRD). Methods Retrospective, single-center, cohort study of 119 consecutive patients (119 eyes) that underwent RRD repair using PPV. The primary outcomes were ERM formation, classified using an optical coherence tomography grading system, and the rate of ERM peeling. Visual acuity, postoperative complications, and risk factors for ERM formation and peeling were also identified. Results Postoperative ERM formation occurred in 69 eyes (58.0%); 56 (47.1%) were stage 1, 9 (7.6%) stage 2, 3 (2.5%) stage 3, and 1 (0.8%) stage 4. Only 6 (5.0%) eyes required secondary PPV for a visually significant ERM, with a mean time to reoperation of 488 ± 351 days. Risk factors for ERM formation included intraoperative cryotherapy, more than 1000 laser shots, 360° laser photocoagulation, and choroidal detachment (p < 0.01). Eyes with more than 3 tears had a trend towards increased ERM surgery (p = 0.10). Conclusions Visually significant ERM formation following PPV for primary RRD was uncommon in this cohort (5%). Half of the ERMs were detected after the first post-operative year, indicating that this complication may be underreported in studies with only 1-year follow-up.

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