Researchers found that fetal MRI analysis with machine learning yielded 82% accuracy, 80% sensitivity and 84% specificity in identifying infants who need to undergo cerebrospinal fluid diversion after birth. The approach, described in JAMA Pediatrics, also had 91% accuracy, 75% sensitivity and 95% specificity in determining candidates for postnatal CSF diversion in a replicated cohort model.
Fetal MRI, machine learning show promise in predicting postnatal CSF diversion
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