ARTIFICIAL INTELLIGENCE FOR OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY-BASED DISEASE ACTIVITY PREDICTION IN AGE-RELATED MACULAR DEGENERATION.

Autor:	Heinke A; Department of Ophthalmology at the Shiley Eye Institute, University of California at San Diego La Jolla, California.; Joan and Irwin Jacobs Retina Center, La Jolla, California., Zhang H; Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California; and., Deussen D; Department of Ophthalmology at the Shiley Eye Institute, University of California at San Diego La Jolla, California.; Joan and Irwin Jacobs Retina Center, La Jolla, California.; University Eye Hospital, Ludwig-Maximillians-University, Munich, Germany., Galang CMB; Department of Ophthalmology at the Shiley Eye Institute, University of California at San Diego La Jolla, California.; Joan and Irwin Jacobs Retina Center, La Jolla, California., Warter A; Department of Ophthalmology at the Shiley Eye Institute, University of California at San Diego La Jolla, California.; Joan and Irwin Jacobs Retina Center, La Jolla, California., Kalaw FGP; Department of Ophthalmology at the Shiley Eye Institute, University of California at San Diego La Jolla, California.; Joan and Irwin Jacobs Retina Center, La Jolla, California., Bartsch DG; Department of Ophthalmology at the Shiley Eye Institute, University of California at San Diego La Jolla, California.; Joan and Irwin Jacobs Retina Center, La Jolla, California., Cheng L; Department of Ophthalmology at the Shiley Eye Institute, University of California at San Diego La Jolla, California.; Joan and Irwin Jacobs Retina Center, La Jolla, California., An C; Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California; and., Nguyen T; Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California; and., Freeman WR; Department of Ophthalmology at the Shiley Eye Institute, University of California at San Diego La Jolla, California.; Joan and Irwin Jacobs Retina Center, La Jolla, California.; Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California; and.
Jazyk:	angličtina
Zdroj:	Retina (Philadelphia, Pa.) [Retina] 2024 Mar 01; Vol. 44 (3), pp. 465-474.
DOI:	10.1097/IAE.0000000000003977
Abstrakt:	Purpose: The authors hypothesize that optical coherence tomography angiography (OCTA)-visualized vascular morphology may be a predictor of choroidal neovascularization status in age-related macular degeneration (AMD). The authors thus evaluated the use of artificial intelligence (AI) to predict different stages of AMD disease based on OCTA en face 2D projections scans. Methods: Retrospective cross-sectional study based on collected 2D OCTA data from 310 high-resolution scans. Based on OCT B-scan fluid and clinical status, OCTA was classified as normal, dry AMD, wet AMD active, and wet AMD in remission with no signs of activity. Two human experts graded the same test set, and a consensus grading between two experts was used for the prediction of four categories. Results: The AI can achieve 80.36% accuracy on a four-category grading task with 2D OCTA projections. The sensitivity of prediction by AI was 0.7857 (active), 0.7142 (remission), 0.9286 (dry AMD), and 0.9286 (normal) and the specificity was 0.9524, 0.9524, 0.9286, and 0.9524, respectively. The sensitivity of prediction by human experts was 0.4286 active choroidal neovascularization, 0.2143 remission, 0.8571 dry AMD, and 0.8571 normal with specificity of 0.7619, 0.9286, 0.7857, and 0.9762, respectively. The overall AI classification prediction was significantly better than the human (odds ratio = 1.95, P = 0.0021). Conclusion: These data show that choroidal neovascularization morphology can be used to predict disease activity by AI; longitudinal studies are needed to better understand the evolution of choroidal neovascularization and features that predict reactivation. Future studies will be able to evaluate the additional predicative value of OCTA on top of other imaging characteristics (i.e., fluid location on OCT B scans) to help predict response to treatment.
Databáze:	MEDLINE
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