  Facial analysis technologies have recently measured up to the capabilities of
expert clinicians in syndrome identification. To date, these technologies could
only identify phenotypes of a few diseases, limiting their role in clinical
settings where hundreds of diagnoses must be considered.
  We 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.
  We suggest that this form of artificial intelligence is ready to support
medical genetics in clinical and laboratory practices and will play a key role
in the future of precision medicine.
