AI-Based Visual Prognosis in Full-Thickness Macular Hole Surgery Using the ILM Flap Technique

Lukpan Orazbekov, Kairat Ruslanuly, Elmira Kanafyanova, Altynai Kairatkyzy Kairat

Data confidence 70 medium

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Abstract

Objectives: This study evaluated the clinical utility of a multimodal large language model-based artificial intelligence (AI) model in predicting postoperative visual outcomes following full-thickness macular hole (FTMH) surgery using the inverted internal limiting membrane (ILM) flap technique. Methods: A retrospective analysis was conducted on 45 patients who underwent pars plana vitrectomy for FTMH at a tertiary eye care center between January 2021 and December 2023. Preoperative optical coherence tomography (OCT) images, demographic data, and clinical parameters were analyzed using the AI model to predict best-corrected visual acuity (BCVA) at four postoperative time points. The predicted BCVA values were then compared with actual clinical outcomes. Results: =0.1949, p=0.199). Conclusion: These findings demonstrate the model's potential as a supportive tool for visual outcome prediction after FTMH surgery. The AI platform showed clinically relevant predictive performance for preoperative and later postoperative visual outcomes after FTMH surgery, particularly at 6 and 12 months, but was less reliable in the early postoperative period.