Research team improves fetal heart defect detection using machine learning

UC San Francisco researchers have found a way to double doctors’ accuracy in detecting the vast majority of complex fetal heart defects in utero—when interventions could either correct them or greatly improve a child’s chance of survival—by combining routine ultrasound imaging with machine-learning computer tools.

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