Facial recognition technology highly beneficial in genetic medicine

DeepGestalt achieved 91% accuracy in identifying the correct syndrome on 502 images representing over 200 different genetic syndromes, and outperformed expert clinicians in three experiments.

JANUARY 7, 2019 18:45

An illustration of Israeli-founded company FDNA's DeepGestalt-powered Face2Gene platform (photo credit: Courtesy) — An illustration of Israeli-founded company FDNA's DeepGestalt-powered Face2Gene platform

(photo credit: Courtesy)

Using deep-learning technology to identify facial features of genetic disorders could add significant value to clinical genetics evaluations and precision medicine, a study published by the Israeli-founded company FDNA and leading scientists has revealed.

The research, published Monday in the peer-reviewed journal Nature Medicine, discussed FDNA’s “DeepGestalt” deep-learning technology, a novel facial-analysis framework that highlights the facial phenotypes of hundreds of diseases and genetic variations.

The practice of facial dysmorphology – the study of congenital structural malformations – has played a significant role in geneticist practice for decades.

Cofounded in 2011 by Moti Shniberg and Prof. Lior Wolf, FDNA’s DeepGestalt-powered Face2Gene platform is currently used by more than 70% of geneticists around the world and has evaluated over 100,000 patients across 2,000 clinical sites in more than 130 countries.

In the study, DeepGestalt achieved 91% accuracy in identifying the correct syndrome on 502 images representing over 200 different genetic syndromes, and outperformed expert clinicians in three additional experiments.

The technology adds significant value, the research showed, to phenotypic evaluations in clinical genetics, genetic testing, research, and precision medicine, and should be used in tandem with next-generation DNA sequencing for optimal results.

“This is a long-awaited breakthrough in medical genetics that has finally come to fruition,” said Dr. Karen Gripp, FDNA chief medical officer 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.”

The platform has the potential to revolutionize the identification of genetic diseases. A total of 6-8% of the global population is estimated to have one of more than 7,000 known rare diseases, of which approximately 80% are genetic in origin. Most of the diseases affect children.

“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 FDNA CEO Dekel Gelbman.

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“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.”