Automatic face classification of Cushing's syndrome in women - a novel screening approach

Abstract

Objective: Cushing's syndrome causes considerable harm to the body if left untreated, yet often remains undiagnosed for prolonged periods of time. In this study we aimed to test whether face classification software might help in discriminating patients with Cushing's syndrome from healthy controls.

Design: Diagnostic study.

Patients: Using a regular digital camera, we took frontal and profile pictures of 20 female patients with Cushing's syndrome and 40 sex- and age-matched controls.

Measurements: Semi-automatic analysis of the pictures was performed by comparing texture and geometry within a grid of nodes placed on the pictures. The leave-one-out cross-validation method was employed to classify subjects by the software.

Results: The software correctly classified 85.0% of patients and 95.0% of controls, resulting in a total classification accuracy of 91.7%.

Conclusions: In this preliminary analysis we found a good classification accuracy of Cushing's syndrome by face classification software. Testing accuracy is comparable to that of currently employed screening tests.