Artificial Intelligence-Based Technology Quickly Identifies Genetic Causes of Serious Disease

An artificial intelligence (AI)-based technology rapidly diagnoses rare disorders in critically ill children with high accuracy, according to a report scientists from University of Utah Health and Fabric Genomics, collaborators on a study led Rady Children’s Hospital in San DiegoThe benchmark finding, published in Genomic Medicine, foreshadows the next phase of medicine, where technology helps clinicians quickly determine the root cause of disease so they can give patients the right treatment sooner.

“This study is an exciting milestone demonstrating how rapid insights from AI-powered decision support technologies have the potential to significantly improve patient care,” says Mark Yandell, Ph.D., co-corresponding author on the paperYandell is a professor of human genetics and Edna Benning Presidential Endowed Chair at U of U Health, and a founding scientific advisor to Fabric.

Worldwide, about seven million infants are born with serious genetic disorders each yearFor these children, life usually begins in intensive careA handful of NICUs in the U.S., including at U of U Health, are now searching for genetic causes of disease reading, or sequencing, the three billion DNA letters that make up the human genomeWhile it takes hours to sequence the whole genome, it can take days or weeks of computational and manual analysis to diagnose the illness.

For some infants, that is not fast enough, Yandell saysUnderstanding the cause of the newborn’s illness is critical for effective treatmentArriving at a diagnosis within the first 24 to 48 hours after birth gives these patients the best chance to improve their conditionKnowing that speed and accuracy are essential, Yandell’s group worked with Fabric to develop the new Fabric GEM algorithm, which incorporates AI to find DNA errors that lead to disease.

In this study, the scientists tested GEM analyzing whole genomes from 179 previously diagnosed pediatric cases from Rady’s Children’s Hospital and five other medical centers from across the worldGEM identified the causative gene as one of its top two candidates 92% of the timeDoing so outperformed existing tools that accomplished the same task less than 60% of the time.

“DrYandell and the Utah team are at the forefront of applying AI research in genomics,” says Martin Reese, Ph.D., CEO of Fabric Genomics and a co-author on the paper“Our collaboration has helped Fabric achieve an unprecedented level of accuracy, opening the door for broad use of AI-powered whole genome sequencing in the NICU.”

GEM leverages AI to learn from a vast and ever-growing body of knowledge that has become challenging to keep up with for clinicians and scientistsGEM cross-references large databases of genomic sequences from diverse populations, clinical disease information, and other repositories of medical and scientific data, combining all this with the patient’s genome sequence and medical recordsTo assist with the medical record search, GEM can be coupled with a natural language processing tool, Clinithink’s CLiX focus, which scans reams of doctors’ notes for the clinical presentations of the patient’s disease.

“Critically ill children rapidly accumulate many pages of clinical notes,” Yandell says“The need for physicians to manually review and summarize note contents as part of the diagnostic process is a massive time sinkThe ability of Clinithink’s tool to automatically convert the contents of these notes in seconds for consumption GEM is critical for speed and scalability.”

Existing technologies mainly identify small genomic variants that include single DNA letter changes, or insertions or deletions of a small string of DNA lettersBy contrast, GEM can also find “structural variants” as causes of diseaseThese changes are larger and are often more complexIt’s estimated that structural variants are behind 10 to 20% of genetic disease.

“To be able to diagnose with more certainty opens a new frontier,” says Luca Brunelli, M.D., neonatologist and professor of pediatrics at U of U Health, who leads a team using GEM and other genome analysis technologies to diagnose patients in the NICUHis goal is to provide answers to families who would have had to live with uncertainty before the development of these toolsHe says these advances now provide an explanation for why a child is sick, enable doctors to improve disease management, and, at times, lead to recovery.

“This is a major innovation, one made possible through AI,” Yandell says“GEM makes genome sequencing more cost-effective and scalable for NICU applicationsIt took an international team of clinicians, scientists, and software engineers to make this happenSeeing GEM at work for such a critical application is gratifying.”

Fabric and Yandell’s team at the Utah Center for Genetic Discovery have had their collaborative research supported several national agencies, including the National Institutes of Health and American Heart Association, and the U of U’s Center for Genomic MedicineYandell will continue to advise the Fabric team to further optimize GEM’s accuracy and interface for use in the clinic.

De La Vega FM, Chowdhury S, Moore B, Frise E, McCarthy J, Hernandez EJ, Wong T, James K, Guidugli L, Agrawal PB, Genetti CA, Brownstein CA, Beggs AH, Löscher BS, Franke A, Boone B, Levy SE, Õunap K, Pajusalu S, Huentelman M, Ramsey K, Naymik M, Narayanan V, Veeraraghavan N, Billings P, Reese MG, Yandell M, Kingsmore SF.
Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases.
Genome Med2021 Oct 14;13(1):153doi: 10.1186/s13073-021-00965-0

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