Deep Learning for the Detection of Multiple Fundus Diseases Using Ultra-widefield Images.

ABSTRACT:

Introduction

To design and evaluate a deep learning model based on ultra-widefield images (UWFIs) that can detect several common fundus diseases.

Methods

Based on 4574 UWFIs, a deep learning model was trained and validated that can identify normal fundus and eight common fundus diseases, namely referable diabetic retinopathy, retinal vein occlusion, pathologic myopia, retinal detachment, retinitis pigmentosa, age-related macular degeneration, vitreous opacity, and optic neuropathy. The model was tested on three test sets with data volumes of 465, 979, and 525. The performance of the three deep learning networks, EfficientNet-B7, DenseNet, and ResNet-101, was evaluated on the internal test set. Additionally, we compared the performance of the deep learning model with that of

SUBMITTER: Sun G 

PROVIDER: S-EPMC10011259 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature