Automated detection of glaucoma with interpretable machine learning using clinical data and multi-modal retinal images.

Purpose To develop a multi-modal model to automate glaucoma detection. Design Development of a machine-learning glaucoma detection model. Methods We selected a study cohort from the UK Biobank dataset with 1193 eyes of 863 healthy subjects and 1283 eyes of 771 subjects with glaucoma. We trained a multi-modal model that combines multiple deep neural nets, trained on macular optical coherence tomography volumes and color fundus photos, with demographic and clinical data. We performed an interpretability analysis to identify features the model relied on to detect glaucoma. We determined the importance of different features in detecting glaucoma using interpretable machine learning methods. We also evaluated the model on subjects who did not have a diagnosis of glaucoma on the day of imaging but were later diagnosed (progress-to-glaucoma, PTG). Results Results show that a multi-modal model that combines imaging with demographic and clinical features is highly accurate (AUC 0.97). Interpretation of this model highlights biological features known to be related to the disease, such as age, intraocular pressure, and optic disc morphology. Our model also points to previously unknown or disputed features, such as pulmonary function and retinal outer layers. Accurate prediction in PTG highlights variables that change with progression to glaucoma - age and pulmonary function. Conclusions The accuracy of our model suggests distinct sources of information in each imaging modality and in the different clinical and demographic variables. Interpretable machine learning methods elucidate subject-level prediction and help uncover the factors that lead to accurate predictions, pointing to potential disease mechanisms or variables related to the disease.