Rapid, AI-Powered Genetic Disease Diagnosis for Critically Ill Infants

Rapid, AI-Powered Genetic Disease Diagnosis for Critically Ill Infants

The researchers at Rady’s Children’s Institute for Genomic Medicine (RCIGM) are known for working quickly. In February of last year, they broke the Guinness World Record for the fastest diagnosis through whole genome sequencing, with an average of 19 hours.

Continuing that work, they have built an automated pipeline to analyze data from electronic health records and genome sequences from dried blood spots to deliver a potential diagnosis for hospitalized, often critically ill, children with suspected genetic diseases. The pipeline required minimal user intervention, increases usability and shortened time to diagnosis, delivering a provisional finding in a median time of less than 24 hours.

Stephen Kingsmore, MD Rady’s Children’s Institute for Genomic Medicine

The work is published in Science Translational Medicine in a paper titled, “Diagnosis of genetic diseases in seriously ill children by rapid whole-genome sequencing and automated phenotyping and interpretation.”

“This is truly pioneering work by the RCIGM team–saving the lives of very sick newborn babies by using AI to rapidly and accurately analyze their whole genome sequence ” says Eric Topol, MD, Professor of Molecular Medicine at Scripps Research.

“Using machine-learning platforms doesn’t replace human experts. Instead it augments their capabilities,” said Michelle Clark, PhD, statistical scientist at RCIGM and the first author of the study. “By informing timely targeted treatments, rapid genome sequencing can improve the outcomes of seriously ill children with genetic diseases.”

The technologies involved include a rapid Whole Genome Sequencing (rWGS) process to screen a child’s entire genetic makeup for thousands of genetic anomalies from a blood sample. Key components in the rWGS pipeline come from Illumina, including Nextera DNA Flex library preparation, whole genome sequencing via the NovaSeq 6000 and the S1 flow cell format. Other pipeline elements include Clinithink’s clinical natural language processing platform CliX ENRICH that quickly combs through a patient’s electronic medical record to automatically extract crucial phenotype information. Another core element of the machine learning system is MOON by Diploid. The platform automates genome interpretation using AI to automatically filter and rank likely pathogenic variants. Deep phenotype integration, based on natural language processing of the medical literature, is one of the key features driving this automated interpretation. MOON takes five minutes to suggest the causal mutation out of the 4.5 million variants in a whole genome. In addition, Alexion’s rare disease and data science expertise enabled the translation of clinical information into a computable format for guided variant interpretation.

The genetic sequencing data was fed into automated computational platforms under the supervision of researchers. For comparison and verification, clinical medical geneticists on the team used Fabric Genomics’ AI-based algorithms–VAAST and Phevor integrated into the clinical decision support software, OPAL (now called Fabric Enterprise)–to confirm the output of the automated pipeline.

“Some people call this artificial intelligence, we call it augmented intelligence,” said Stephen Kingsmore, MD, DSc, President and CEO of RCIGM. “Patient care will always begin and end with the doctor. By harnessing the power of technology, we can quickly and accurately determine the root cause of genetic diseases. We rapidly provide this critical information to intensive care physicians so they can focus on personalizing care for babies who are struggling to survive.”

An estimated four percent of newborns in North America are affected by genetic diseases, which are the leading cause of death in infants. Rare genetic diseases also account for approximately 15 percent of admissions to children’s hospitals.

Increased automation of the process removes a barrier to scaling up clinical use of WGS by reducing the need for time-consuming manual analysis and interpretation of the data by scarce certified clinical medical geneticists. Although this pipeline would need to be adapted for use at different hospital systems, such an automated tool could aid clinicians to expedite an accurate genetic disease diagnosis, potentially hastening lifesaving changes to patient care. This new method opens the door to increased use of genome sequencing as a first-line diagnostic test for babies with cryptic conditions.

 

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I'M ADODO TOLULOPE FELIX, RN. I'M A NURSE, A WRITER, AN ENTREPRENEUR, AN AGRICULTURIST. MUCH EXPERIENCED IN GENERAL NURSING PRACTICE. CREATED THIS PLATFORM TO UPGRADE MY DEAR PROFESSION IN THE GREATEST WAY I CAN.

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