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AI can identify genetic disorders by the shape of someone’s face

New research from FDNA, a leader in artificial intelligence and precision medicine, suggests that facial analysis can now detect genetic disorders much more efficiently.

Credit: FDNA

This new technology, the scientists who worked on the study said, will add significant value in personalised care and will become a standard among deep learning based genomic tools.

The paper is called “Identifying Facial Phenotypes of Genetic Disorders Using Deep Learning”, and was published in the peer-reviewed journal Nature Medicine on January 07, 2019 as the product of three years of research.

The deep learning technology discussed is named DeepGestalt, and it is an innovative 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 standardised 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.”

DeepGestalt was trained on a dataset of over 150,000 patients, curated through Face2Gene, a community-driven phenotyping platform. For this study, 17,000 patient images representing more than 200 syndromes were used.

“Artificial intelligence is the life force of personalised 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.”


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