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Date: Jan 30, 2019

Identifying Facial Phenotypes of Genetic Disorders Using Deep Learning The deep learning technology discussed, in the DeepGestalt paper is a novel facial analysis framework that highlights the facial phenotypes of hundreds of diseases and genetic variations.

 “This is a long-awaited breakthrough in medical genetics that has finally come to fruition,” said Dr. Karen Gripp, CMO at FDNA and co-author of the paper. “With this study, we’ve shown that adding an automated facial analysis framework, such as DeepGestalt, to the clinical workflow can help achieve earlier diagnosis and treatment, and promise an improved quality of life.”

Yaron Gurovich, CTO at FDNA and first author of the paper, added, “The increased ability to describe phenotype in a standardized way opens the door to future research and applications, and the identification of new genetic syndromes. It demonstrates how one can successfully apply state of the art algorithms, such as deep learning, to a challenging field where the available data is small, unbalanced in terms of available patients per condition, and where the need to support a large amount of conditions is great.”
This technology transforms next-generation phenotyping (NGP)—the capture, structuring, and analysis of complex human physiological data—and is trained on a dataset of over 150,000 patients, curated through Face2Gene, a community-driven phenotyping platform. For the purposes of this study, 17,000 patient images representing more than 200 syndromes were used.
Findings from the study include:
DeepGestalt achieves 91% top-10-accuracy in identifying the correct syndrome on 502 images;
outperformed expert clinicians in three additional experiments;
adds significant value to phenotypic evaluations in clinical genetics, genetic testing, research, and precision medicine;
should be used in tandem with next-generation sequencing (NGS) for optimal results
“Artificial intelligence is the life force of personalized care, with genome sequencing well on its way to becoming a standard protocol in precision medicine,” said Dekel Gelbman, CEO of FDNA. “For years, we’ve relied solely on the ability of medical professionals to identify genetically linked disease. We’ve finally reached a reality where this work can be augmented by AI, and we’re on track to continue developing leading AI frameworks using clinical notes, medical images, and video and voice recordings to further enhance phenotyping in the years to come.”
The original paper can be found here.
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