Researchers at Rady Children\u2019s Institute for Genomic Medicine (RCIGM) have utilized automated machine-learning and clinical natural language processing (CNLP) to diagnose rare genetic diseases in record time. This new method is speeding answers to physicians caring for infants in intensive care and opening the door to increased use of genome sequencing as a first-line diagnostic test for babies with cryptic conditions.<\/p>\n
\u201cSome people call this artificial intelligence, we call it augmented intelligence,\u201d said Stephen Kingsmore, MD, DSc, President and CEO of RCIGM. \u201cPatient 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.\u201d<\/p>\n
The workflow and research were led by the RCIGM team in collaboration with leading technology and data-science developers \u2014Alexion, Clinithink, Diploid, Fabric Genomics and Illumina.<\/p>\n
Dr. Kingsmore\u2019s team has pioneered a rapid Whole Genome Sequencing process to deliver genetic test results to neonatal and paediatric intensive care (NICU\/PICU) physicians to guide medical intervention. RCIGM is the research arm of Rady Children\u2019s Hospital-San Diego.<\/p>\n
By reducing the need for labour-intensive manual analysis of genomic data, the supervised automated pipeline provided significant time-savings. In February 2018, the same team achieved the Guinness World Record for fastest diagnosis through whole genome sequencing. Of the automated runs, the fastest times \u2013 averaging 19 hours \u2013 were achieved using augmented intelligence.<\/p>\n
\u201cThis is truly pioneering work by the RCIGM team\u2014saving the lives of very sick newborn babies by using AI to rapidly and accurately analyse their whole genome sequence \u201c says Eric Topol, MD, Professor of Molecular Medicine at Scripps Research and author of the new book Deep Medicine.<\/p>\n
RCIGM has optimized and integrated several time-saving technologies into a rapid Whole Genome Sequencing (rWGS) process to screen a child\u2019s entire genetic makeup for thousands of genetic anomalies from a blood sample.<\/p>\n
Key components in the rWGS pipeline come from Illumina, the global leader in DNA sequencing, including Nextera DNA Flex library preparation, whole genome sequencing via the NovaSeq 6000 and the S1 flow cell format. Speed and accuracy are enhanced by Illumina\u2019s DRAGEN (Dynamic Read Analysis for GENomics) Bio-IT Platform.<\/p>\n
Other pipeline elements include Clinithink\u2019s clinical natural language processing platform CliX ENRICH that quickly combs through a patient\u2019s electronic medical record to automatically extract comprehensive patient phenotype information.<\/p>\n
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.<\/p>\n
In addition, Alexion\u2019s rare disease and data science expertise enabled the translation of clinical information into a computable format for guided variant interpretation.<\/p>\n
As part of this study, 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\u2019 AI-based clinical decision support software, OPAL (now called Fabric Enterprise)\u2014to confirm the output of the automated pipeline. Fabric software is part of RCIGM\u2019s standard analysis and interpretation workflow.<\/p>\n
The study titled \u201cDiagnosis of genetic diseases in seriously ill children by rapid whole-genome sequencing and automated phenotyping and interpretation,\u201d found that automated, retrospective diagnoses concurred with expert manual interpretation (97 percent recall, 99 percent precision in 95 children with 97 genetic diseases).<\/p>\n
Researchers concluded that genome sequen-cing with automated phenotyping and interpretation\u2014in a median 20:10 hours\u2014may spur use in intensive care units, thereby enabling timely and precise medical care. \u201cUsing machine-learning platforms doesn\u2019t replace human experts. Instead it augments their capabilities,\u201d said Michelle Clark, PhD, statistical scientist at RCIGM and the first author of the study. \u201cBy informing timely targeted treatments, rapid genome sequencing can improve the outcomes of seriously ill children with genetic diseases.\u201d
\nRady Children\u2019s Institutewww.radygenomics.org\/category\/news\/pr\/<\/link>\n","protected":false},"excerpt":{"rendered":"
Researchers at Rady Children\u2019s Institute for Genomic Medicine (RCIGM) have utilized automated machine-learning and clinical natural language processing (CNLP) to diagnose rare genetic diseases in record time. This new method is speeding answers to physicians caring for infants in intensive care and opening the door to increased use of genome sequencing as a first-line diagnostic […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[35],"tags":[],"class_list":["post-724","post","type-post","status-publish","format-standard","hentry","category-e-news"],"_links":{"self":[{"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/posts\/724"}],"collection":[{"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/comments?post=724"}],"version-history":[{"count":0,"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/posts\/724\/revisions"}],"wp:attachment":[{"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/media?parent=724"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/categories?post=724"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/tags?post=724"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}