AI Uses Face Scans to Diagnose Rare Genetic Disorders

The system can be deployed on a smartphone, achieves 91 percent top-10-accuracy in identifying over 215 different genetic syndromes, and has outperformed clinical experts in three separate experiments. The FDNA team's research paper, Identifying facial phenotypes of genetic disorders using deep learning, has been published in Nature Medicine. Using deep learning algorithms and brain-like neural networks, the Face3Gene app can predict congenital and neural developmental disorders in people through the detection of distinctive facial features in photos. Face2Gene builds on a technique the FDNA team introduced last January in the paper DeepGestalt -- Identifying Rare Genetic Syndromes Using Deep Learning. Researchers started by training an AI system to distinguish two conditions which cause distinct facial features -- Cornelia de Langs syndrome and Angelman syndrome -- from other, similar conditions.