A CNN-aided method to predict glaucoma progression using DARC (Detection of Apoptosing Retinal Cells).

We describe here an automatic method of DARC spot detection which was developed using a CNN which was trained and tested on the control cohort of subjects in the Phase 2 DARC trial. The CNN algorithm was found to have a 97.0% accuracy, 91.1% sensitivity and 97.1% specificity to spot detection when compared to manual grading of 50% controls.Subsequently, the CNN algorithm was tested on glaucoma patients in the trial, using gold standard optical coherence tomography (OCT) global rates of progression (retinal nerve fibre layer at 3.5 ring) eighteen months after their assessment with DARC. Those patients with a significant (p<0.05) negative slope were defined as progressing compared to those without who were defined as stable. The CNN algorithm had a sensitivity of 85.7% and specificity of 91.7% to glaucoma progression, with an AUC of 0.89.Finally, the CNN algorithm was found to show a significantly (p=0.0044) greater number of DARC positively stained cells in the progressing compared to stable glaucoma groups.This paper describes the successful use of a CNN-aided algorithm which automates detection of apoptosis with DARC enabling prediction of glaucoma progression 18 months later. We believe this method provides an automated and objective biomarker with potentially widespread clinical applications. PMID: 32310684 [PubMed - as supplied by publisher]
Source: Expert Review of Molecular Diagnostics - Category: Laboratory Medicine Tags: Expert Rev Mol Diagn Source Type: research