Differentiation of Diabetic Status Using Statistical and Machine Learning Techniques on Optical Coherence Tomography Angiography Images

PURPOSE: To investigate the potential of statistical and machine learning approaches to determine the diabetic status of patients from optical coherence tomography angiography (OCT-A) images. METHODS: This was a retrospective cross-sectional observational study based at Manchester Royal Eye Hospital, United Kingdom. OCT-A scans were sequentially selected from one eye of each of 182 patients who were either not diabetic, diabetic without retinopathy, or diabetic with retinopathy requiring hospital follow-up. Eligible images were analyzed by expert purpose-built automated algorithms to calculate clinically relevant outcome measures. These were used in turn as inputs to machine learning and statistical procedures to derive algorithms to perform clinically relevant classifications of patient images into the clinical groups. Receiver operating characteristic curves for the classifiers were evaluated and predictive accuracy assessed using area under curve (AUC). RESULTS: For distinguishing diabetic patients from those without diabetes, the Random Forest classifier provided the highest AUC (0.8). For distinguishing diabetic patients with significant retinopathy from those with no retinopathy, the highest AUC was represented by logistic regression (0.91). CONCLUSIONS: The study demonstrates the potential of novel techniques using automated analysis of OCT-A scans to diagnose patients with diabetes, or when diabetic status is known, to automatically determine those that require hospital input. TRANSLATIONAL RELEVANCE: This work advances the concept of a rapid and noninvasive clinical screening tool using OCT-A to determine a patient's diabetic status.