FDNA's Face2Gene RESEARCH uses computer vision to accelerate genomic discoveries

The application enables clinicians all around the world to make rare and genetic disease discoveries using deep learning and artificial intelligence.

FDNA's Face2Gene RESEARCH uses computer vision to accelerate genomic discoveries

FDNA has launched the Face2Gene RESEARCH, an application in the Face2Gene Suite that uses facial analysis, deep learning and artificial intelligence (AI) to analyze patient cohorts to make genomic discoveries.

With this offering, FDNA hopes to help clinicians test scientific hypotheses using the de-identified data from the patients they routinely see, thus paving the way for advancement in research on genomics and rare diseases at an unprecedented pace. The analysis instantly identifies the facial characteristics, phenotypes and genes that are potentially indicative of syndromes, to increase understanding of diseases and support diagnostic and therapeutic research.

"The ability for clinicians globally to make discoveries using their patients' de-identified data will provide, for the first time, a global understanding of diseases representative of real-world patient populations," Dekel Gelbam, CEO of FDNA, said in a statement. "We are proud to offer this application to healthcare professionals, free of charge."

The launch of Face2Gene RESEARCH is part of FDNA's Year of Discovery, an initiative to unite clinicians, labs and patients worldwide to make rare disease discoveries, with a special focus on specific rare disease categories each month of 2017. Every case analyzed by Face2Gene trains the deep learning system to better recognize syndrome-related phenotypes and disease-causing genes while also reporting new findings to support the user's research and publications.

Face2Gene RESEARCH enables researchers to apply new technologies to enhance their studies, leverage data from its research community, and engage in collaborative studies to yield greater genomic insights.

"One in 10 people in the U.S. suffer from a rare disease and it takes, on average, seven years for a patient to be accurately diagnosed," Gelbman added. "We aim to end the diagnostic odyssey and give hope to children and families battling rare diseases."

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