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.

Fabric Genomics and Rady Children's Institute for Genomic Medicine today announced the publication of a retrospective study in Genome Medicine showing that across six leading genomic centers and hospitals, researchers were able to detect more than 90% of disease-causing variants in infants with rare diseases using the Fabric GEM AI algorithm and whole-genome and whole-exome data from previously diagnosed newborns and rare disease patients at Rady Children's Hospital – San Diego and other clinical sites. Despite differences in case collection, sequencing methods, and bioinformatics pipelines across all sites, Fabric GEM's performance demonstrated a new standard of accuracy, ranking the causative variant first or second more than 90% of the time. In addition, Fabric GEM ranked specific diseases and conditions associated with these genes to assist clinicians in the ultimate diagnosis of each case. These findings demonstrate how artificial intelligence (AI) can successfully reduce the burden of gene variant review by clinical geneticists. "Fast and definitive genetic diagnosis is essential to providing the right treatment in a timely manner for critically ill newborns," said Stephen Kingsmore, MD, DSc, a co-author of the study and the President and CEO of Rady Children's Institute for Genomic Medicine.

Clinical interpretation of genetic variants in the context of the patient’s phenotype is becoming the largest component of cost and time expenditure for genome-based diagnosis of rare genetic diseases. Artificial intelligence (AI) holds promise to greatly simplify and speed genome interpretation by integrating predictive methods with the growing knowledge of genetic disease. Here we assess the diagnostic performance of Fabric GEM, a new, AI-based, clinical decision support tool for expediting genome interpretation. We benchmarked GEM in a retrospective cohort of 119 probands, mostly NICU infants, diagnosed with rare genetic diseases, who received whole-genome or whole-exome sequencing (WGS, WES). We replicated our analyses in a separate cohort of 60 cases collected from five academic medical centers. For comparison, we also analyzed these cases with current state-of-the-art variant prioritization tools. Included in the comparisons were trio, duo, and singleton cases. Variants underpinning diagnoses spanned diverse modes of inheritance and types, including structural variants (SVs). Patient phenotypes were extracted from clinical notes by two means: manually and using an automated clinical natural language processing (CNLP) tool. Finally, 14 previously unsolved cases were reanalyzed. GEM ranked over 90% of the causal genes among the top or second candidate and prioritized for review a median of 3 candidate genes per case, using either manually curated or CNLP-derived phenotype descriptions. Ranking of trios and duos was unchanged when analyzed as singletons. In 17 of 20 cases with diagnostic SVs, GEM identified the causal SVs as the top candidate and in 19/20 within the top five, irrespective of whether SV calls were provided or inferred ab initio by GEM using its own internal SV detection algorithm. GEM showed similar performance in absence of parental genotypes. Analysis of 14 previously unsolved cases resulted in a novel finding for one case, candidates ultimately not advanced upon manual review for 3 cases, and no new findings for 10 cases. GEM enabled diagnostic interpretation inclusive of all variant types through automated nomination of a very short list of candidate genes and disorders for final review and reporting. In combination with deep phenotyping by CNLP, GEM enables substantial automation of genetic disease diagnosis, potentially decreasing cost and expediting case review.

We all wish to be different or unique in our lives, hoping to be seen for our strengths from the rest. However, some of us are born not with rare gifts, but unfortunately with rare diseases. More than 350 million people across the world are living with 1 of the 7000 rare genetic diseases (RD) discovered, 75% of which are children. Despite these staggering numbers, rare genetic disorders are severely underemphasized in the medical field as each disorder must follow an extensive and expensive detecting and treatment process that many patients across the world do not have access to. Out of these diseases, only 5% come with a cure and the majority are unable to be effectively diagnosed to offset their deadly symptoms.

But in some work environments, like medicine, mistakes can be deadly. That's why more and more medical personnel are turning to artificial intelligence (AI) to help reduce the rate of error. Although, many experienced doctors are skeptical about using AI in medicine, researchers around the globe are working on new ways to apply it. The options are diverse -- and in some cases rather peculiar. The AI technology is a lot more precise than the human nose in analyzing a person's breath Human breath contains numerous chemicals that can be helpful in the diagnosis of different diseases.

When Shayla Haddock's doctors tested her for a rare genetic disease in 2012, they couldn't pinpoint a diagnosis. Her lifelong symptoms -- which include club feet, short stature, unusual facial features and congenital deafness -- led her doctors to suspect a disease-causing gene mutation. But for children like Shayla, finding the culprit among 3 billion base pairs of DNA can be very difficult. Each case takes 20 to 40 hours of analysis by a trained geneticist after gene sequencing has been done, and around 75 percent of patients don't get a diagnosis on the first try. As I described in a recent story, Shayla's case was eventually solved by a team of Stanford computer scientists who devised an automated way to compare patients' symptoms and mutated genes to information in existing databases of genetic diseases.

Jill Viles, an Iowa mother, was born with a rare type of muscular dystrophy. The symptoms weren't really noticeable until preschool, when she began to fall while walking. She saw doctors, but they couldn't diagnose her or supply a remedy. When she left for college, she was 5-foot-3 and weighed just 87 pounds. How she would spend her time there turned into part of a remarkable story by David Epstein, published in ProPublica in January.