A novel approach to the detection of acromegaly: accuracy of diagnosis by automatic face classification.

CONTEXT: The delay between onset of first symptoms and diagnosis of the acromegaly is 6-10 yr. Acromegaly causes typical changes of the face that might be recognized by face classification software.
OBJECTIVE: The objective of the study was to assess classification accuracy of acromegaly by face-classification software.
DESIGN: This was a diagnostic study.
SETTING: The study was conducted in specialized care. PARTICIPANTS: Participants in the study included 57 patients with acromegaly (29 women, 28 men) and 60 sex- and age-matched controls.
INTERVENTIONS: We took frontal and side photographs of the faces and grouped patients into subjects with mild, moderate, and severe facial features of acromegaly by overall impression. We then analyzed all pictures using computerized similarity analysis based on Gabor jets and geometry functions. We used the leave-one-out cross-validation method to classify subjects by the software. Additionally, all subjects were classified by visual impression by three acromegaly experts and three general internists. MAIN OUTCOME MEASURE: Classification accuracy by software, experts, and internists was measured.
FINDINGS: The software correctly classified 71.9% of patients and 91.5% of controls. Classification accuracy for patients by visual analysis was 63.2 and 42.1% by experts and general internists, respectively. Classification accuracy for controls was 80.8 and 87.0% by experts and internists, respectively. The highest differences in accuracy between software and experts and internists were present for patients with mild acromegaly.
CONCLUSIONS: Acromegaly can be detected by computer software using photographs of the face. Classification accuracy by software is higher than by medical experts or general internists, particularly in patients with mild features of acromegaly. This is a promising tool to help detecting acromegaly.