Mital Shah1,2, Ana Roomans Ledo3, Jens Rittscher3. 1. Oxford Eye Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK. 2. Nuffield Laboratory of Ophthalmology, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK. 3. Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.
Abstract
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.
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.
Authors: Alexandra Miere; Olivia Zambrowski; Arthur Kessler; Carl-Joe Mehanna; Carlotta Pallone; Daniel Seknazi; Paul Denys; Francesca Amoroso; Eric Petit; Eric H Souied Journal: J Clin Med Date: 2021-12-08 Impact factor: 4.241
Authors: Nihaal Mehta; Cecilia S Lee; Luísa S M Mendonça; Khadija Raza; Phillip X Braun; Jay S Duker; Nadia K Waheed; Aaron Y Lee Journal: JAMA Ophthalmol Date: 2020-10-01 Impact factor: 8.253