
via Medical Xpress
Researchers at Rady Children’s 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.
“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.”
A new study documenting the process was published today in the journal Science Translational Medicine. The workflow and research were led by the RCIGM team in collaboration with leading technology and data-science developers —Alexion, Clinithink, Diploid, Fabric Genomics and Illumina.
Dr. Kingsmore’s team has pioneered a rapid Whole Genome Sequencing process to deliver genetic test results to neonatal and pediatric intensive care (NICU/PICU) physicians to guide medical intervention. RCIGM is the research arm of Rady Children’s Hospital-San Diego.
By reducing the need for labor-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 – averaging 19 hours – were achieved using augmented intelligence.
“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 and author of the new book Deep Medicine.
RCIGM has optimized and integrated several time-saving technologies into 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, 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’s DRAGEN (Dynamic Read Analysis for GENomics) Bio-IT Platform.
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 comprehensive patient 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.
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’ AI-based clinical decision support software, OPAL (now called Fabric Enterprise)—to confirm the output of the automated pipeline. Fabric software is part of RCIGM’s standard analysis and interpretation workflow.
The study titled “Diagnosis of genetic diseases in seriously ill children by rapid whole-genome sequencing and automated phenotyping and interpretation,” found that automated, retrospective diagnoses concurred with expert manual interpretation (97 percent recall, 99 percent precision in 95 children with 97 genetic diseases).
Researchers concluded that genome sequencing with automated phenotyping and interpretation—in a median 20:10 hours—may spur use in intensive care units, thereby enabling timely and precise medical care.
“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.”
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.
The RCIGM workflow is engineered to speed and scale up genomic data interpretation to reduce the time and cost of whole genome sequencing. The team’s goal is to make rWGS accessible and available to any child who needs it.
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. There were fewer than 1,600 of these experts nationwide in 2017, according to the American Board of Medical Genetics and Genomics.
Rady Children’s Institute began performing genomic sequencing in July 2016. As of the end of March 2019, the team had completed testing and interpretation of the genomes of more than 750 children. One-third of those children have received a genetic diagnosis with 25 percent of those benefitting from an immediate change in clinical care based on their diagnosis.
Read more: Machine-learning system used to diagnose genetic diseases
The Latest on: Using artificial intelligence to diagnose genetic diseases
via Google News
The Latest on: Using artificial intelligence to diagnose genetic diseases
- Artificial intelligence could reduce time to diagnose breast canceron March 2, 2021 at 4:11 pm
Google Health and Northwestern Medicine are to explore whether artificial intelligence could prioritise reviews of mammograms higher risk of breast cancer.
- Preimplantation Genetic Diagnosis Market: Rising Preference for Single Gene Disorder Screening over Prenatal Diagnostic Testing to Drive Marketon February 24, 2021 at 1:31 am
The Asia preimplantation genetic diagnosis market has a highly competitive vendor landscape, observes Transparency Market Research (TMR). The market ...
- Take A Look At How Artificial Intelligence Can Improve Your Pet’s Healthon February 22, 2021 at 3:13 pm
Could artificial intelligence give us better insight into our pet's health? How can a data-driven approach help animals live healther, happier lives?
- Rare genetic diseases – what’s in store for 2021?on February 16, 2021 at 1:37 am
There is also the use of artificial intelligence that can diagnose patients in ... We may be able to firmly establish genomic profiling to accelerate the diagnosis of rare genetic diseases. Digital ...
- Artificial intelligence to diagnose concealed long QT syndromeon February 15, 2021 at 7:00 pm
Long QT syndrome (LQTS) is characterized by prolongation of the QT interval and is associated with an increased risk of sudden cardiac death. However, although QT interval prolongation is ...
- Artificial Intelligence Identifies Lung Cancer Patients at Risk of Harm Caused by Immunotherapyon February 9, 2021 at 12:00 am
Using artificial intelligence (AI ... senior author on the study, aim to diagnose and characterize cancers and other diseases. The team teaches computers to search for and identify patterns ...
- Using Artificial Intelligence to prevent harm caused by immunotherapyon February 6, 2021 at 2:22 pm
Now, researchers, using artificial intelligence (AI ... a global leader in the detection, diagnosis and characterization of various cancers and other diseases by meshing medical imaging, machine ...
- Using Artificial Intelligence to prevent harm caused by immunotherapyon February 4, 2021 at 1:50 pm
Researchers at Case Western Reserve University, using artificial intelligence ... in the detection, diagnosis and characterization of various cancers and other diseases by meshing medical imaging ...
- Artificial Intelligence in Healthcare Market – Global Industry Analysis, Size, Share, Growth, Trends and Forecast 2021 - 2026on February 3, 2021 at 10:19 pm
The global Artificial ... Intelligence Laboratory (CSAIL), Harvard Medical School and Massachusetts General Hospital in a collaboration used AI in a model developed to help in the detection and ...
- artificial intelligenceon January 12, 2021 at 1:00 am
These tools, still relatively new, hold the potential to fix gene misspellings—and potentially cure—a wide range of genetic diseases that were once ... developed an artificial intelligence (AI) ...
via Bing News