PT  - JOURNAL ARTICLE
AU  - Abitbol, Elie
AU  - Miere, Alexandra
AU  - Excoffier, Jean-Baptiste
AU  - Mehanna, Carl-Joe
AU  - Amoroso, Francesca
AU  - Kerr, Samuel
AU  - Ortala, Matthieu
AU  - Souied, Eric H
TI  - Deep learning-based classification of retinal vascular diseases using ultra-widefield colour fundus photographs
AID  - 10.1136/bmjophth-2021-000924
DP  - 2022 Feb 01
TA  - BMJ Open Ophthalmology
PG  - e000924
VI  - 7
IP  - 1
4099  - http://bmjophth.bmj.com/content/7/1/e000924.short
4100  - http://bmjophth.bmj.com/content/7/1/e000924.full
SO  - BMJ Open Ophth2022 Feb 01; 7
AB  - Objective To assess the ability of a deep learning model to distinguish between diabetic retinopathy (DR), sickle cell retinopathy (SCR), retinal vein occlusions (RVOs) and healthy eyes using ultra-widefield colour fundus photography (UWF-CFP).Methods and Analysis In this retrospective study, UWF-CFP images of patients with retinal vascular disease (DR, RVO, and SCR) and healthy controls were included. The images were used to train a multilayer deep convolutional neural network to differentiate on UWF-CFP between different vascular diseases and healthy controls. A total of 224 UWF-CFP images were included, of which 169 images were of retinal vascular diseases and 55 were healthy controls. A cross-validation technique was used to ensure that every image from the dataset was tested once. Established augmentation techniques were applied to enhance performances, along with an Adam optimiser for training. The visualisation method was integrated gradient visualisation.Results The best performance of the model was obtained using 10 epochs, with an overall accuracy of 88.4%. For DR, the area under the receiver operating characteristics (ROC) curve (AUC) was 90.5% and the accuracy was 85.2%. For RVO, the AUC was 91.2% and the accuracy 88.4%. For SCR, the AUC was 96.7% and the accuracy 93.8%. For healthy controls, the ROC was 88.5% with an accuracy that reached 86.2%.Conclusion Deep learning algorithms can classify several retinal vascular diseases on UWF-CPF with good accuracy. This technology may be a useful tool for telemedicine and areas with a shortage of ophthalmic care.Data are available upon reasonable request. Data are available upon reasonable request from the corresponding author.