New Technique May Speed Up Disease Detection in Newborns
The combination of a new sequencing technique and machine learning may speed up the diagnosis of diseases in newborns, as well as reduce false positive results, Yale researchers say.
Blood is drawn shortly after a newborn’s birth to screen for a host of preventable diseases, including more than 40 rare but potentially disabling and serious metabolic disorders. The blood tests are highly sensitive and present one minor flaw: In many cases, they indicate a disorder when none is present.
This false positive result causes parents to hit the panic button, at a time when they are already pretty sensitive. By introducing a new screening method, researchers are able to examine an entire metabolic profile, allowing for a more precise analysis than previous techniques.
The new sequencing method used by the researchers correctly identified 89 percent of newborns with methylmalonic acidemia (MMA), an inborn metabolic disorder that can lead to fatal neonatal disease. In addition, by combining the sequencing with machine learning, they were able to reduce MMA false positive results.
The “big picture” plan for the researchers is to test these techniques a bit more, in the hopes they will one day complement existing routine blood work to avoid lengthy testing, as well as speed up the treatment for babies in need of early and additional care.
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