GENERATIVE DEEP LEARNING APPROACH TO PREDICT POSTTREATMENT OPTICAL COHERENCE TOMOGRAPHY IMAGES OF AGE-RELATED MACULAR DEGENERATION AFTER 12 MONTHS.

Journal: Retina (Philadelphia, Pa.)

Published Date: Jun 1, 2025

Abstract

PURPOSE: Predicting long-term anatomical responses in neovascular age-related macular degeneration patients is critical for patient-specific management. This study validates a generative deep learning model to predict 12-month posttreatment optical coherence tomography (OCT) images and evaluates the impact of incorporating clinical data on predictive performance.

Authors

Hyungwoo Lee

Department of Ophthalmology, Konkuk University School of Medicine, Konkuk University Medical Center, Seoul, Republic of Korea.
Najung Kim

Department of Ophthalmology, Konkuk University School of Medicine, Konkuk University Medical Center, Seoul, Republic of Korea.
Na Hee Kim

Department of Ophthalmology, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Republic of Korea ; and.
Hyewon Chung

Department of Obstetrics and Gynecology, Keimyung University School of Medicine, Daegu, South Korea.
Hyung Chan Kim

Kong Eye Hospital, Seoul, Republic of Korea.

Keywords

Aged Aged, 80 and over Angiogenesis Inhibitors Deep Learning Female Fluorescein Angiography Follow-Up Studies Fundus Oculi Humans Intravitreal Injections Macula Lutea Male Ranibizumab Retrospective Studies Time Factors Tomography, Optical Coherence Vascular Endothelial Growth Factor A Visual Acuity Wet Macular Degeneration

External Resources

View on PubMed Access via DOI PubMed (39841905)

GENERATIVE DEEP LEARNING APPROACH TO PREDICT POSTTREATMENT OPTICAL COHERENCE TOMOGRAPHY IMAGES OF AGE-RELATED MACULAR DEGENERATION AFTER 12 MONTHS.

Abstract

Authors

Keywords

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