As the understanding of genetic syndromes deepens, researchers and providers are working to develop new methods of screening children with the goal of earlier diagnoses and treatments.
To the end, researchers at Children’s National Hospital developed a machine learning tool that trained with genetic data from 2,800 pediatric patients from 28 countries and also considers variables related to sex, age, racial, and ethnic backgrounds.
With an average accuracy of 88%, the new technology offers rapid genetic screening that could accelerate the diagnosis of genetic syndromes, recommending further investigation or referral to a specialist in seconds, according to a study published in The Lancet Digital Health.
“We built a software device to increase access to care and a machine learning technology to identify the disease patterns not immediately obvious to the human eye or intuition, and to help physicians non-specialized in genetics,” principal investigator in the Sheikh Zayed Institute for Pediatric Surgical Innovation at Children’s National Hospital and senior author of the study Marius George Linguraru, DPhil, MA, MS, said in a statement.
“This technological innovation can help children without access to specialized clinics, which are unavailable in most of the world. Ultimately, it can help reduce health inequality in under-resourced societies.”
Every year, millions of children are born with genetic disorders, including Down syndrome, Williams-Beuren syndrome, and Noonan syndrome, and experts say most children with genetic syndromes live in areas with limited resources and access to genetic services. Moreover, particularly in isolated communities of low income and limited resources there are insufficient specialists to help identify genetic syndromes early in life when preventive care can save lives.
According to the researchers, the new machine learning technology can indicate the presence of a genetic syndrome from a facial photograph captured at a point-of-care, such as in pediatrician offices, maternity wards, and general practitioner clinics.
“Unlike other technologies, the strength of this program is distinguishing ‘normal’ from ‘not-normal,’ which makes it an effective screening tool in the hands of community caregivers,” said Marshall L. Summar, MD, director of the Rare Disease Institute at Children’s National.
“This can substantially accelerate the time to diagnosis by providing a robust indicator for patients that need further workup. This first step is often the greatest barrier to moving towards a diagnosis. Once a patient is in the workup system, then the likelihood of diagnosis (by many means) is significantly increased.”
As more data from underrepresented groups becomes available, researchers will adapt the model to localize phenotypical variations with more specific demographic groups.
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