Automated diagnosing primary open-angle glaucoma from fundus image by simulating human’s grading with deep learning
Year: 2022
Type: article
Abstract: Primary open-angle glaucoma (POAG) is a leading cause of irreversible blindness worldwide. Although deep learning methods have been proposed to diagnose POAG, it remains challenging to develop a robust and explainable algorithm to automatically facilitate the downstream diagnostic tasks. In this study, we present an automated classification algorithm, GlaucomaNet, to identify POAG using variable fundus photographs from different populations and settings. GlaucomaNet consists of two convolutional neural networks to simulate the human grading process: learning the discriminative features and fusing the features for grading. We evaluated GlaucomaNet on two datasets: Ocular Hypertension Treatment Study (OHTS) participants and the Large-scale Attention-based Glaucoma (LAG) dataset. GlaucomaNet achieved the highest AUC of 0.904 and 0.997 for POAG diagnosis on OHTS and LAG datasets. An ensemble of network architectures further improved diagnostic accuracy. By simulating the human grading process, GlaucomaNet demonstrated high accuracy with increased transparency in POAG diagnosis (comprehensiveness scores of 97% and 36%). These methods also address two well-known challenges in the field: the need for increased image data diversity and relying heavily on perimetry for POAG diagnosis. These results highlight the potential of deep learning to assist and enhance clinical POAG diagnosis. GlaucomaNet is publicly available on https://github.com/bionlplab/GlaucomaNet .
Source: Scientific Reports
Institutions: Weill Cornell Medicine, Washington University in St. Louis
Language: English
Topic: Retinal Imaging and Analysis
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,190
Awards: G1221396913, G1296005322, G2031530228, G2283643561, G2926212704, A framework to enhance radiology structured report by invoking NLP and DL: Models and Applications, Core Grant for Vision Research, Ocular hypertension treatment study (OHTS), G3415720651, Ocular Hypertension Treatment Study 20-Year Follow-up: Clinical Center Grant, G4389040704, G4422846742, G4495713152, Ocular Hypertension Treatment Study 20-Year Follow-up: Coordinating Center Grant, G5029479215, G5226072191, Ocular Hypertension Treatment Study 20-Year Follow-up: Resource Center Grant, G731955937, Ocular Hypertension Treatment Study: Coordinating Center, Ocular Hypertension Treatment Study 20-Year Follow-up: Chairman's Grant, G8599106632, G863188045, G9000111633, G9000284829, G953340708
OpenAlex ID: W4292676168