Preoperative Visual Acuity Thresholds in Pars Plana Vitrectomy for Epiretinal Membrane: A Systematic Review.

BACKGROUND AND OBJECTIVE: This review consolidates findings from studies that used a preoperative visual acuity (VA) threshold as an indication for epiretinal membrane (ERM) surgery. METHODS: The literature was systematically searched using Ovid MEDLINE, EMBASE, and Cochrane Library from January 2000 to October 2022 to select studies reporting on pars plana vitrectomy (PPV) for ERM that used a preoperative VA threshold as an inclusion criterion. Primary outcomes were final best-corrected visual acuity (BCVA) and change in BCVA relative to baseline. Secondary outcomes included risk of intra- and postoperative complications. RESULTS: A total of 639 eyes from seven studies were included. The most liberal preoperative VA threshold was 20/28.5 or worse, whereas the most conservative threshold was worse than 20/60. The mean preoperative BCVA was 0.55 logarithm of the minimum angle of resolution (logMAR) (∼20/70), and the mean postoperative BCVA was 0.35 logMAR (∼20/45). Generally, VA improved relative to baseline, regardless of the preoperative VA threshold. The smallest improvement in VA was observed in a study where the pre-operative VA to consider surgery was liberal (20/30 or worse), whereas the greatest VA improvement was observed in a study that used a conservative preoperative VA threshold (worse than 20/60). CONCLUSIONS: The greatest improvement in BCVA was observed in studies where a conservative pre-operative VA threshold was used. The decision to operate should involve a patient-centered approach with a thorough discussion of the risks and benefits of PPV, regardless of the preoperative VA threshold used. [Ophthalmic Surg Lasers Imaging Retina 2024;55:400-407.].

Accuracy of an Artificial Intelligence Chatbot's Interpretation of Clinical Ophthalmic Images.

Importance: Ophthalmology is reliant on effective interpretation of multimodal imaging to ensure diagnostic accuracy. The new ability of ChatGPT-4 (OpenAI) to interpret ophthalmic images has not yet been explored. Objective: To evaluate the performance of the novel release of an artificial intelligence chatbot that is capable of processing imaging data. Design, Setting, and Participants: This cross-sectional study used a publicly available dataset of ophthalmic cases from OCTCases, a medical education platform based out of the Department of Ophthalmology and Vision Sciences at the University of Toronto, with accompanying clinical multimodal imaging and multiple-choice questions. Across 137 available cases, 136 contained multiple-choice questions (99%). Exposures: The chatbot answered questions requiring multimodal input from October 16 to October 23, 2023. Main Outcomes and Measures: The primary outcome was the accuracy of the chatbot in answering multiple-choice questions pertaining to image recognition in ophthalmic cases, measured as the proportion of correct responses. χ2 Tests were conducted to compare the proportion of correct responses across different ophthalmic subspecialties. Results: A total of 429 multiple-choice questions from 136 ophthalmic cases and 448 images were included in the analysis. The chatbot answered 299 of multiple-choice questions correctly across all cases (70%). The chatbot's performance was better on retina questions than neuro-ophthalmology questions (77% vs 58%; difference = 18%; 95% CI, 7.5%-29.4%; χ21 = 11.4; P < .001). The chatbot achieved a better performance on nonimage-based questions compared with image-based questions (82% vs 65%; difference = 17%; 95% CI, 7.8%-25.1%; χ21 = 12.2; P < .001).The chatbot performed best on questions in the retina category (77% correct) and poorest in the neuro-ophthalmology category (58% correct). The chatbot demonstrated intermediate performance on questions from the ocular oncology (72% correct), pediatric ophthalmology (68% correct), uveitis (67% correct), and glaucoma (61% correct) categories. Conclusions and Relevance: In this study, the recent version of the chatbot accurately responded to approximately two-thirds of multiple-choice questions pertaining to ophthalmic cases based on imaging interpretation. The multimodal chatbot performed better on questions that did not rely on the interpretation of imaging modalities. As the use of multimodal chatbots becomes increasingly widespread, it is imperative to stress their appropriate integration within medical contexts.

Diagnostic methods for primary vitreoretinal lymphoma: A systematic review.

Primary vitreoretinal lymphoma is a potentially aggressive intraocular malignancy with poor systemic prognosis and sometimes significant diagnostic delays as it may masquerade as chronic uveitis. Despite the variety of diagnostic techniques, it is unclear which modality is most accurate in the diagnosis of PVRL. A systematic literature search was conducted on Ovid MEDLINE, EMBASE and the Cochrane Controlled Register of Trials for studies published between January, 2000, and June, 2023. Randomized controlled trials (RCTs) reporting on the following diagnostic tools used to diagnose patients with PVRL were included: cytology, flow cytometry, MYD88 L265P mutation, CD79B mutation, interleukin 10/interleukin-6 (IL-10/IL-6) ratio, polymerase chain reaction (PCR) for monoclonal immunoglobulin heavy chain (IgH) and immunoglobulin kappa light chain (IgK) rearrangements, and imaging findings. The aggregated sensitivity of each diagnostic modality was reported and compared using the chi-squared (χ2) test. A total of 662 eyes from 29 retrospective studies reporting on patients diagnosed with PVRL were included. An IL-10/IL-6 ratio greater than 1 had the highest sensitivity (89.39%, n = 278/311 eyes, n = 16 studies) for PVRL, where the sensitivity was not significantly different when only vitreous samples were drawn (88.89%, n = 232/261 eyes, n = 13 studies) compared to aqueous samples (83.33%, n = 20/24, n = 2) (p = 0.42). Flow cytometry of vitreous samples gave a positive result in 66/75 eyes (88.00%, n = 6 studies) with PVRL, and monoclonal IgH rearrangements on PCR gave a positive result in 354/416 eyes (85.10%, n = 20 studies) with PVRL. MYD88 L265P and CD79B mutation analysis performed poorly, yielding a positive result in 63/90 eyes (70.00%, n = 8 studies) with PVRL, and 20/57 eyes (35.09%, n = 4 studies) with PVRL, respectively. Overall, our systematic review found that an IL-10/IL-6 ratio greater or equal to one may provide the highest sensitivity in identifying patients with PVRL. Future studies are needed to employ multiple diagnostic tools to aid in the detection of PVRL and to further establish nuanced guidelines when determining the optimal diagnostic tool to use in diverse patient populations.