Progression of macular retinoschisis following intravitreal aflibercept injection for myopic macular neovascularization-a case report and review of literature.

BACKGROUND: Macular retinoschisis (MRS) and myopic macular neovascularization (mMNV) are both potentially blinding complications of high myopia. In this case report, we highlight the progression of MRS after intravitreal anti-vascular endothelial growth factor (anti-VEGF) treatment for mMNV, as well as an extensive review of the literature on this topic. CASE DESCRIPTION: A 49-year-old woman presented with two weeks of recent onset blurring and metamorphopsia in her right eye. She had high myopia in both eyes (right eye - 20/60 with - 16D, left eye - 20/20 with - 13D). Slit-lamp ophthalmoscopy found a normal anterior segment in both eyes. On fundus examination, features of pathological myopia with posterior staphyloma and peripapillary atrophy were observed in both eyes. An active mMNV, as well as intraretinal fluid, minimal perifoveal inner and outer MRS, and focal posterior vitreous traction along the inferotemporal retinal arcade, were detected on optical coherence tomography (OCT) of the right eye. The patient received an intravitreal injection of Aflibercept (2 mg/0.05 ml). RESULTS: OCT scans at two- and four-month follow-up visits revealed regressed mMNV with a taut epiretinal membrane, progressive worsening of outer MRS, and the development of multiple perifoveal retinal detachment inferior to the fovea. Pars plana vitrectomy surgery was performed for the progressive MRS with good anatomical (resolved MRS) and functional outcome (maintained visual acuity at 20/60) at the last one-month post-surgery visit. CONCLUSION: Intravitreal anti-VEGF injections for mMNV can cause vitreoretinal interface changes, exacerbating MRS and causing visual deterioration. Vitrectomy for MRS could be one of several treatment options.

Recommendations for diabetic macular edema management by retina specialists and large language model-based artificial intelligence platforms.

PURPOSE: To study the role of artificial intelligence (AI) in developing diabetic macular edema (DME) management recommendations by creating and comparing responses to clinicians in hypothetical AI-generated case scenarios. The study also examined whether its joint recommendations followed national DME management guidelines. METHODS: The AI hypothetically generated 50 ocular case scenarios from 25 patients using keywords like age, gender, type, duration and control of diabetes, visual acuity, lens status, retinopathy stage, coexisting ocular and systemic co-morbidities, and DME-related retinal imaging findings. For DME and ocular co-morbidity management, we calculated inter-rater agreements (kappa analysis) separately for clinician responses, AI-platforms, and the "majority clinician response" (the maximum number of identical clinician responses) and "majority AI-platform" (the maximum number of identical AI responses). Treatment recommendations for various situations were compared to the Indian national guidelines. RESULTS: For DME management, clinicians (ĸ=0.6), AI platforms (ĸ=0.58), and the 'majority clinician response' and 'majority AI response' (ĸ=0.69) had moderate to substantial inter-rate agreement. The study showed fair to substantial agreement for ocular co-morbidity management between clinicians (ĸ=0.8), AI platforms (ĸ=0.36), and the 'majority clinician response' and 'majority AI response' (ĸ=0.49). Many of the current study's recommendations and national clinical guidelines agreed and disagreed. When treating center-involving DME with very good visual acuity, lattice degeneration, renal disease, anaemia, and a recent history of cardiovascular disease, there were clear disagreements. CONCLUSION: For the first time, this study recommends DME management using large language model-based generative AI. The study's findings could guide in revising the global DME management guidelines.

Recommendations for initial diabetic retinopathy screening of diabetic patients using large language model-based artificial intelligence in real-life case scenarios.

PURPOSE: To study the role of artificial intelligence (AI) to identify key risk factors for diabetic retinopathy (DR) screening and develop recommendations based on clinician and large language model (LLM) based AI platform opinions for newly detected diabetes mellitus (DM) cases. METHODS: Five clinicians and three AI applications were given 20 AI-generated hypothetical case scenarios to assess DR screening timing. We calculated inter-rater agreements between clinicians, AI-platforms, and the "majority clinician response" (defined as the maximum number of identical responses provided by the clinicians) and "majority AI-platform" (defined as the maximum number of identical responses among the 3 distinct AI). Scoring was used to identify risk factors of different severity. Three, two, and one points were given to risk factors requiring screening immediately, within a year, and within five years, respectively. After calculating a cumulative screening score, categories were assigned. RESULTS: Clinicians, AI platforms, and the "majority clinician response" and "majority AI response" had fair inter-rater reliability (k value: 0.21-0.40). Uncontrolled DM and systemic co-morbidities required immediate screening, while family history of DM and a co-existing pregnancy required screening within a year. The absence of these risk factors required screening within 5 years of DM diagnosis. Screening scores in this study were between 0 and 10. Cases with screening scores of 0-2 needed screening within 5 years, 3-5 within 1 year, and 6-12 immediately. CONCLUSION: Based on the findings of this study, AI could play a critical role in DR screening of newly diagnosed DM patients by developing a novel DR screening score. Future studies would be required to validate the DR screening score before it could be used as a reference in real-life clinical situations. CLINICAL TRIAL REGISTRATION: Not applicable.