Abstract
Researchers based at the Children's National Hospital in Washington have created a facial recognition tool using machine-learning to help clinicians diagnose rare genetic syndromes faster.
The tool was built based on a collection of 2,800 photographs of children's faces. Of these, 1,400 had 128 known genetic conditions, such as Down syndrome and Noonan syndrome, and 1,400 were controls without genetic syndromes, matched for age, sex, and ethnicity.
The machine learning program developed by the researchers is able to recognize small, common differences in facial features common to specific syndromes that a clinician may not pick up in a consultation, particularly if they do not have specialist knowledge.
The overall accuracy of the model for correctly detecting a genetic syndrome was 88%, with 90% sensitivity and 86% specificity. The tool was designed to account for normal facial variation in a given population, but accuracy of prediction was greater in White and Hispanic children at 90% and 91%, respectively. Accuracy in African and Asian groups was lower at respective rates of 84% and 82%.