A novel management challenge in age-related macular degeneration: Artificial intelligence and expert prediction of geographic atrophy.

Autor:	Reiter GS; Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria., Lachinov D; Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria., Bühl W; Vienna Clinical Trial Center (VTC), Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria., Weigert G; Vienna Clinical Trial Center (VTC), Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria., Grechenig C; Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria., Mai J; Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria., Bogunović H; Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria., Schmidt-Erfurth U; Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria; Vienna Clinical Trial Center (VTC), Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria. Electronic address: ursula.schmidt-erfurth@meduniwien.ac.at.
Jazyk:	angličtina
Zdroj:	Ophthalmology. Retina [Ophthalmol Retina] 2024 Nov 08. Date of Electronic Publication: 2024 Nov 08.
DOI:	10.1016/j.oret.2024.10.029
Abstrakt:	Purpose: The progression of geographic atrophy (GA) secondary to age-related macular degeneration (AMD) is highly variable among individuals. Prediction of the progression is critical to identify patients who will benefit most from the first treatments currently approved. The aim of this study was the investigation of the value and difference in predictive power between ophthalmologists and artificial intelligence (AI) to reliably assess individual speed of GA progression. Design: Prospective, expert and AI comparison study. Participants: Eyes with natural progression of GA from a prospective study (NCT02503332). Methods: Ophthalmologists predicted yearly growth speed of GA, as well as selecting the potentially faster growing lesions from two eyes based on fundus autofluorescence (FAF), near-infrared reflectance (NIR), and optical coherence tomography (OCT). A deep learning algorithm predicted progression solely on the baseline OCT (Spectralis, Heidelberg Engineering, Germany). Main Outcome Measures: Accuracy, weighted kappa (κ), and concordance index (c-index) between the prediction made by ophthalmology specialists, ophthalmology residents and the AI. Results: A total of 134 eyes of 134 patients from a phase II clinical trial were included, among those 53 were from the sham arm and 81 from untreated fellow eyes. 2880 gradings were performed by four ophthalmologists. Human experts reached an accuracy of 0.37, 0.43, 0.41 and a κ of 0.06, 0.16, 0.18 on FAF, NIR+OCT and FAF+NIR+OCT, respectively. On a pairwise comparison task, human experts achieved a c-index of 0.62, 0.59 and 0.60. Automated AI-based analysis reached an accuracy of 0.48 and a κ of 0.23 on the first task, and a c-index of 0.69 on the second task solely utilizing OCT imaging. Conclusions: Prediction of individual progression will become an important task for patient counseling, most importantly with treatments becoming available. Human gradings improved with the availability of OCT. However, automated AI performed better than ophthalmologists in several comparisons. AI-supported decisions improve clinical precision, access to timely care for the community, and socioeconomic feasibility in the management of the leading cause for irreversible vision loss. (Copyright © 2024. Published by Elsevier Inc.)
Databáze:	MEDLINE
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