Andrew was playing under the summer sun in the backyard. As the four-year-old's parents watched, they noticed something seemed off. Perhaps it was his unusually small head or the after-effects of the surgery to correct his congenital disorder. When Andrew's parents consulted Dr. Karen Gripp, Professor of Pediatrics at Nemours Children's Hospital, she decided to investigate. In addition to conventional procedures, she ran a quick diagnosis on Face2Gene, a computer vision-powered app that looks for indications of rare diseases.

In 2012, Moti Shniberg sold his face recognition startup to Facebook and started looking for a new challenge. "We wanted to take our expertise and do something good," he says. Then he met the head of a medical genetics center, who explained the difficulty of diagnosing rare genetic disorders in children. Specialists sometimes use the shape and appearance of a child's face as a clue because some conditions, such as Down syndrome, give a child's face a distinctive appearance. For many other diseases, however, the signs are more subtle, and the cases very rare.

To prevent the leakage of private information while enabling automated machine intelligence, there is an emerging trend to integrate federated learning and Neural Architecture Search (NAS). Although promising as it may seem, the coupling of difficulties from both two tenets makes the algorithm development quite challenging. In particular, how to efficiently search the optimal neural architecture directly from massive non-iid data of clients in a federated manner remains to be a hard nut to crack. To tackle this challenge, in this paper, by leveraging the advances in proxy-less NAS, we propose a Federated Direct Neural Architecture Search (FDNAS) framework that allows hardware-aware NAS from decentralized non-iid data of clients. To further adapt for various data distributions of clients, inspired by meta-learning, a cluster Federated Direct Neural Architecture Search (CFDNAS) framework is proposed to achieve client-aware NAS, in the sense that each client can learn a tailored deep learning model for its particular data distribution. Extensive experiments on real-world non-iid datasets show state-of-the-art accuracy-efficiency trade-offs for various hardware and data distributions of clients. Our codes will be released publicly upon paper acceptance.

And now, an algorithm can predict not only whether they carry a genetic mutation, but which genes were mutated. The study, published Monday in Nature Medicine, is the latest from a Boston-based company called FDNA, one of a few organizations creating software that can help physicians diagnose genetic syndromes based just on a face -- and may serve an important validation of the company's technology, said Yaron Gurovich, the company's chief technology officer. "We went for this high-impact journal to prove beyond any doubt that this technology is good, it performs as we say, we can stand behind it, and now it opens a lot of doors to publish more," he said. The study itself is a collection of experiments testing how the results of algorithms -- FDNA refers to them as DeepGestalt -- stack up against clinicians' diagnoses. In one of the experiments, DeepGestalt's performance was better than random chance when picking which of five genetic mutations might be causing a condition called Noonan syndrome.

Facial recognition can help unlock your phone. Could it also be able to play a far more valuable role in people's lives by identifying whether or not a person has a rare genetic disorder, based exclusively on their facial features? DeepGestalt, an artificial intelligence built by the Boston-based tech company FDNA, suggests that the answer is a resounding "yes." The algorithm is already being used by leading geneticists at more than 2,000 sites in upward of 130 countries around the world. In a new study, published in the journal Nature Medicine, researchers show how the algorithm was able to outperform clinicians when it came to identifying diseases.

FDNA, a leader in artificial intelligence and precision medicine, in collaboration with a team of influential scientists and researchers published a milestone study on the use of facial analysis in detecting genetic disorders. The findings in this paper suggest that this type of technology adds significant value in personalized care and will become a standard among deep learning based genomic tools. The paper, titled "Identifying Facial Phenotypes of Genetic Disorders Using Deep Learning", was published in the peer-reviewed journal Nature Medicine (January 07, 2019) as the product of three years of research. The deep learning technology discussed, DeepGestaltTM, 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.

A facial recognition scan could become part of a standard medical checkup in the not-too-distant future. Researchers have shown how algorithms can help identify facial characteristics linked to genetic disorders, potentially speeding up clinical diagnoses. In a study published this month in the journal Nature Medicine, US company FDNA published new tests of their software, DeepGestalt. Just like regular facial recognition software, the company trained their algorithms by analyzing a dataset of faces. FDNA collected more than 17,000 images covering 200 different syndromes using a smartphone app it developed named Face2Gene.

The photograph is cropped close on the face of four-year-old Yael, who is smiling and looking as healthy as can be. But a computer analysis of her features says something's not right. She has MR XL Bain Type, the computer predicts--a very rare syndrome that causes a wide range of health problems. It turned out that the computer was right. Yael is one of thousands of children who have contributed to the development of an artificial intelligence system called DeepGestalt that can identify rare genetic disorders based on facial features alone.

NEW YORK (CBSNewYork) – Could artificial intelligence help doctors diagnose diseases just by looking at faces? When you hear facial recognition you probably think about crime fighting or Homeland Security screening for terrorists. It turns out a person's face can tell a lot about their genetic makeup and the medical conditions it may cause, and a computer can learn to read those differences, CBS2's Dr. Max Gomez reported. "We've learned, surprisingly, that many thousands of genetic rare diseases have a unique facial appearances," Dekel Gelbman told Gomez. Gelbman is the CEO of artificial intelligence company FDNA.

Researchers are improving the ability of algorithms to help spot the physical characteristics of conditions such as Cornelia de Lange syndrome.Credit: Michael Ares/The Palm Beach Post via ZUMA A deep-learning algorithm is helping doctors and researchers to pinpoint a range of rare genetic disorders by analysing pictures of people's faces. In a paper1 published on 7 January in Nature Medicine, researchers describe the technology behind the diagnostic aid, a smartphone app called Face2Gene. It relies on machine-learning algorithms and brain-like neural networks to classify distinctive facial features in photos of people with congenital and neurodevelopmental disorders. Using the patterns that it infers from the pictures, the model homes in on possible diagnoses and provides a list of likely options. Doctors have been using the technology as an aid, even though it's not intended to provide definitive diagnoses.