Year: 2021
Type: article
Abstract: Purpose To propose a reference standard for the definition of glaucomatous optic neuropathy (GON) consisting of objective parameters from spectral-domain optical coherence tomography (SDOCT) and standard automated perimetry (SAP), and to apply it to the development and evaluation of a deep learning (DL) algorithm to detect glaucomatous damage on fundus photographs. Design Retrospective, cross-sectional study. Methods Data were extracted from the Duke Glaucoma Registry and included 2,927 eyes of 2,025 participants with fundus photos, SDOCT and SAP acquired within six months. Eyes were classified as GON versus normal based on a combination of objective SDOCT and SAP criteria. A DL convolutional neural network was trained to predict the probability of GON from fundus photos. The algorithm was tested on an independent sample with performance assessed by sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and likelihood ratios (LR). Results The test sample included 585 eyes of 405 participants. The median DL probability of glaucoma in eyes with GON was 99.8% versus 0.03% for normal eyes (P < 0.001), with an AUC of 0.92 and sensitivity of 77% at 95% specificity. LRs indicated that the DL algorithm provided large changes in the post-test probability of disease for the majority of eyes. Conclusions The DL algorithm had high performance to discriminate eyes with GON from normal. The newly proposed objective definition of GON used as reference standard may increase the comparability of diagnostic studies of glaucoma across devices and populations, helping to improve the development and assessment of tests in clinical practice.
Source: Scientific Reports
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: $1,990
OpenAlex ID: W3125775661