AI Detects Rare Syndromes From Images

Rare disorders often present with patterned discoloration of the epidermis, distortion of features and other visibly-detectable aberrations in appearance. Marfan’s syndrome presents with long, flexible body type. Noonan syndrome may present wide-set eyes, and Down’s syndrome is well-known to nearly everyone. Now, researchers have developed a facial analysis framework, DeepGestalt, using computer vision and deep learning algorithms, that quantifies similarities to hundreds of genetic syndromes based on unconstrained 2D images. DeepGestalt is currently trained with over 26,000 patient cases from a rapidly growing phenotype-genotype database, consisting of tens of thousands of validated clinical cases, curated through a community-driven platform. DeepGestalt currently achieves 91% top-10-accuracy in identifying over 215 different genetic syndromes and has outperformed clinical experts in three separate experiments.

In results published in Nature Medicine, DeepGestalt  outperformed doctors in diagnosing patients with Angelman syndrome and Cornelia de Lange syndrome versus other disorders, and in separating patients with different genetic subtypes of Noonan syndrome.

It’s a neat study in that it controls for a bunch of conditions including ethnicity and gender, so it’s a bit more robust than previous studies.

 

Homework: [PDF] https://arxiv.org/abs/1801.07637