Linking Function and Structure with ReSensNet: Predicting Retinal Sensitivity from OCT using Deep Learning.

Autor:	Seeböck P; OPTIMA - Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria; Department of Ophthalmology and Optometry, Vienna Reading Center, Medical University of Vienna, Vienna, Austria., Vogl WD; OPTIMA - Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria., Waldstein SM; OPTIMA - Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria., Orlando JI; OPTIMA - Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria; Yatiris Group, PLADEMA Institute, UNICEN, CONICET, Tandil, Argentina., Baratsits M; Department of Ophthalmology and Optometry, Vienna Clinical Trial Center, Medical University of Vienna, Vienna, Austria., Alten T; OPTIMA - Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria., Arikan M; OPTIMA - Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria., Mylonas G; Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria., Bogunović H; OPTIMA - Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria., Schmidt-Erfurth U; OPTIMA - Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria; Department of Ophthalmology and Optometry, Vienna Reading Center, Medical University of Vienna, Vienna, Austria. Electronic address: ursula.schmidt-erfurth@meduniwien.ac.at.
Jazyk:	angličtina
Zdroj:	Ophthalmology. Retina [Ophthalmol Retina] 2022 Jun; Vol. 6 (6), pp. 501-511. Date of Electronic Publication: 2022 Feb 05.
DOI:	10.1016/j.oret.2022.01.021
Abstrakt:	Purpose: The currently used measures of retinal function are limited by being subjective, nonlocalized, or taxing for patients. To address these limitations, we sought to develop and evaluate a deep learning (DL) method to automatically predict the functional end point (retinal sensitivity) based on structural OCT images. Design: Retrospective, cross-sectional study. Subjects: In total, 714 volumes of 289 patients were used in this study. Methods: A DL algorithm was developed to automatically predict a comprehensive retinal sensitivity map from an OCT volume. Four hundred sixty-three spectral-domain OCT volumes from 174 patients and their corresponding microperimetry examinations (Nidek MP-1) were used for development and internal validation, with a total of 15 563 retinal sensitivity measurements. The patients presented with a healthy macula, early or intermediate age-related macular degeneration, choroidal neovascularization, or geographic atrophy. In addition, an external validation was performed using 251 volumes of 115 patients, comprising 3 different patient populations: those with diabetic macular edema, retinal vein occlusion, or epiretinal membrane. Main Outcome Measures: We evaluated the performance of the algorithm using the mean absolute error (MAE), limits of agreement (LoA), and correlation coefficients of point-wise sensitivity (PWS) and mean sensitivity (MS). Results: The algorithm achieved an MAE of 2.34 dB and 1.30 dB, an LoA of 5.70 and 3.07, a Pearson correlation coefficient of 0.66 and 0.84, and a Spearman correlation coefficient of 0.68 and 0.83 for PWS and MS, respectively. In the external test set, the method achieved an MAE of 2.73 dB and 1.66 dB for PWS and MS, respectively. Conclusions: The proposed approach allows the prediction of retinal function at each measured location directly based on an OCT scan, demonstrating how structural imaging can serve as a surrogate of visual function. Prospectively, the approach may help to complement retinal function measures, explore the association between image-based information and retinal functionality, improve disease progression monitoring, and provide objective surrogate measures for future clinical trials. (Copyright © 2022 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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