Diagnostic performance and generalizability of deep learning for multiple retinal diseases using bimodal imaging of fundus photography and optical coherence tomography.

ABSTRACT:

Purpose

To develop and evaluate deep learning (DL) models for detecting multiple retinal diseases using bimodal imaging of color fundus photography (CFP) and optical coherence tomography (OCT), assessing diagnostic performance and generalizability.

Methods

This cross-sectional study utilized 1445 CFP-OCT pairs from 1,029 patients across three hospitals. Five bimodal models developed, and the model with best performance (Fusion-MIL) was tested and compared with CFP-MIL and OCT-MIL. Models were trained on 710 pairs (Maestro device), validated on 241, and tested on 255 (dataset 1). Additional tests used different devices and scanning patterns: 88 pairs (dataset 2, DRI-OCT), 91 (dataset 3, DRI-OCT), 60 (dataset 4, Visucam/VG200 OCT). Seven retinal conditions, including normal, d

SUBMITTER: Gu X 

PROVIDER: S-EPMC12460420 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature