A CNN-aided method to predict glaucoma progression using DARC (Detection of Apoptosing Retinal Cells).
Expert Rev Mol Diagn
; 20(7): 737-748, 2020 07.
Article
en En
| MEDLINE
| ID: mdl-32310684
ABSTRACT
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.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Células Ganglionares de la Retina
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Algoritmos
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Glaucoma
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Redes Neurales de la Computación
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Apoptosis
Tipo de estudio:
Clinical_trials
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Diagnostic_studies
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Guideline
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Prognostic_studies
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Risk_factors_studies
Límite:
Adult
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Aged
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Expert Rev Mol Diagn
Asunto de la revista:
BIOLOGIA MOLECULAR
Año:
2020
Tipo del documento:
Article
País de afiliación:
Reino Unido