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Automated classification of normal and Stargardt disease optical coherence tomography images using deep learning
Author(s) -
Shah Mital,
Roomans Ledo Ana,
Rittscher Jens
Publication year - 2020
Publication title -
acta ophthalmologica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.534
H-Index - 87
eISSN - 1755-3768
pISSN - 1755-375X
DOI - 10.1111/aos.14353
Subject(s) - optical coherence tomography , artificial intelligence , convolutional neural network , deep learning , jaccard index , pattern recognition (psychology) , classifier (uml) , retinal , computer science , medicine , ophthalmology
Purpose Recent advances in deep learning have seen an increase in its application to automated image analysis in ophthalmology for conditions with a high prevalence. We wanted to identify whether deep learning could be used for the automated classification of optical coherence tomography (OCT) images from patients with Stargardt disease (STGD) using a smaller dataset than traditionally used. Methods Sixty participants with STGD and 33 participants with a normal retinal OCT were selected, and a single OCT scan containing the centre of the fovea was selected as the input data. Two approaches were used: Model 1 – a pretrained convolutional neural network (CNN); Model 2 – a new CNN architecture. Both models were evaluated on their accuracy, sensitivity, specificity and Jaccard similarity score (JSS). Results About 102 OCT scans from participants with a normal retinal OCT and 647 OCT scans from participants with STGD were selected. The highest results were achieved when both models were implemented as a binary classifier: Model 1 – accuracy 99.6%, sensitivity 99.8%, specificity 98.0% and JSS 0.990; Model 2 – accuracy 97.9%, sensitivity 97.9%, specificity 98.0% and JSS 0.976. Conclusion The deep learning classification models used in this study were able to achieve high accuracy despite using a smaller dataset than traditionally used and are effective in differentiating between normal OCT scans and those from patients with STGD. This preliminary study provides promising results for the application of deep learning to classify OCT images from patients with inherited retinal diseases.