Deep Learning Approach Predicts Longitudinal Retinal Nerve Fiber Layer Thickness Changes.

• Tác giả: Akram Belghith, Christopher Bowd, Mark Christopher, Carlos Gustavo De Moraes, Massimo A Fazio, Christopher A Girkin, Michael H Goldbaum, Jalil Jalili, Jeffrey M Liebmann, Evan Walker, Robert N Weinreb, Linda M Zangwill
• Ngôn ngữ: eng
• Ký hiệu phân loại: 616.55 *Pigmentary changes
• Thông tin xuất bản: Switzerland : Bioengineering (Basel, Switzerland) , 2025
• Bộ sưu tập: NCBI
• ID: 680738

Tóm tắt

This study aims to develop deep learning (DL) models to predict the retinal nerve fiber layer (RNFL) thickness changes in glaucoma, facilitating the early diagnosis and monitoring of disease progression. Using the longitudinal data from two glaucoma studies (Diagnostic Innovations in Glaucoma Study (DIGS) and African Descent and Glaucoma Evaluation Study (ADAGES)), we constructed models using optical coherence tomography (OCT) scans from 251 participants (437 eyes). The models were trained to predict the RNFL thickness at a future visit based on previous scans. We evaluated four models: linear regression (LR), support vector regression (SVR), gradient boosting regression (GBR), and a custom 1D convolutional neural network (CNN). The GBR model achieved the best performance in predicting pointwise RNFL thickness changes (MAE = 5.2 μm, R