Abstract #86751 Published in IGR 21-2

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

Normando EM; Yap TE; Maddison J; Miodragovic S; Bonetti P; Almonte M; Mohammad NG; Ameen S; Ameen S; Crawley L; Ahmed F; Bloom PA; Cordeiro MF
Expert review of molecular diagnostics 2020; 20: 737-748

BACKGROUND: A key objective in glaucoma is to identify those at risk of rapid progression and blindness. Recently, a novel first-in-man method for visualising apoptotic retinal cells called DARC (Detection-of-Apoptosing-Retinal-Cells) was reported. The aim was to develop an automatic CNN-aided method of DARC spot detection to enable prediction of glaucoma progression. METHODS: Anonymised DARC images were acquired from healthy control (n=40) and glaucoma (n=20) Phase 2 clinical trial subjects (ISRCTN10751859) from which 5 observers manually counted spots. The CNN-aided algorithm was trained and validated using manual counts from control subjects, and then tested on glaucoma eyes. RESULTS: The algorithm had 97.0% accuracy, 91.1% sensitivity and 97.1% specificity to spot detection when compared to manual grading of 50% controls. It was next tested on glaucoma patient eyes defined as progressing or stable based on a significant (p<0.05) rate of progression using OCT-retinal nerve fibre layer measurements at 18 months. It demonstrated 85.7% sensitivity, 91.7% specificity with AUC of 0.89, and a significantly (p=0.0044) greater DARC count in those patients who later progressed. CONCLUSION: This CNN-enabled algorithm provides an automated and objective measure of DARC, promoting its use as an AI-aided biomarker for predicting glaucoma progression and testing new drugs.

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6.20 Progression (Part of: 6 Clinical examination methods)
6.30 Other (Part of: 6 Clinical examination methods)
6.9.5 Other (Part of: 6 Clinical examination methods > 6.9 Computerized image analysis)
2.13 Retina and retinal nerve fibre layer (Part of: 2 Anatomical structures in glaucoma)

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