The role of artificial intelligence in macular hole management: A scoping review.

Autor: Mikhail D; Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Department of Ophthalmology, University of Montreal, Montreal, Canada., Milad D; Department of Ophthalmology, University of Montreal, Montreal, Canada; Department of Ophthalmology, Hôpital Maisonneuve-Rosemont, Montreal, Canada; Department of Ophthalmology, Centre Hospitalier de l'Université de Montréal (CHUM), Montreal, Canada., Antaki F; Department of Ophthalmology, University of Montreal, Montreal, Canada; Department of Ophthalmology, Centre Hospitalier de l'Université de Montréal (CHUM), Montreal, Canada., Hammamji K; Department of Ophthalmology, University of Montreal, Montreal, Canada; Department of Ophthalmology, Centre Hospitalier de l'Université de Montréal (CHUM), Montreal, Canada., Qian CX; Department of Ophthalmology, University of Montreal, Montreal, Canada; Department of Ophthalmology, Hôpital Maisonneuve-Rosemont, Montreal, Canada., Rezende FA; Department of Ophthalmology, University of Montreal, Montreal, Canada; Department of Ophthalmology, Hôpital Maisonneuve-Rosemont, Montreal, Canada., Duval R; Department of Ophthalmology, University of Montreal, Montreal, Canada; Department of Ophthalmology, Hôpital Maisonneuve-Rosemont, Montreal, Canada. Electronic address: renaud.duval@gmail.com.
Jazyk: angličtina
Zdroj: Survey of ophthalmology [Surv Ophthalmol] 2024 Sep 30. Date of Electronic Publication: 2024 Sep 30.
DOI: 10.1016/j.survophthal.2024.09.003
Abstrakt: Narrative Abstract: We focus on the utility of artificial intelligence (AI) in the management of macular hole (MH). We synthesize 25 studies, comprehensively reporting on each AI model's development strategy, validation, tasks, performance, strengths, and limitations. All models analyzed ophthalmic images, and 5 (20 %) also analyzed clinical features. Study objectives were categorized based on 3 stages of MH care: diagnosis, identification of MH characteristics, and postoperative predictions of hole closure and vision recovery. Twenty-two (88 %) AI models underwent supervised learning, and the models were most often deployed to determine a MH diagnosis. None of the articles applied AI to guiding treatment plans. AI model performance was compared to other algorithms and to human graders. Of the 10 studies comparing AI to human graders (i.e., retinal specialists, general ophthalmologists, and ophthalmology trainees), 5 (50 %) reported equivalent or higher performance. Overall, AI analysis of images and clinical characteristics in MH demonstrated high diagnostic and predictive accuracy. Convolutional neural networks comprised the majority of included AI models, including those which were high performing. Future research may consider validating algorithms to propose personalized treatment plans and explore clinical use of the aforementioned algorithms.
Competing Interests: Declaration of Competing Interest None
(Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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