Health IT Analytics April 12, 2024
A deep learning model accurately identified false positive screening mammograms, significantly reducing diagnostic callbacks and unnecessary biopsies.
Researchers have developed a deep learning tool capable of reducing false positives without missing true cases of breast cancer identified by screening mammography, according to a study published this week in Radiology: Artificial Intelligence.
Effective cancer screening is key to improving patient outcomes, but medical images like mammograms can be challenging for clinicians to read, potentially leading to false positives.
“False positives are when you call a patient back for additional testing, and it turns out to be benign,” explained senior author Richard L. Wahl, MD, a professor of radiology at Washington University’s Mallinckrodt Institute of Radiology (MIR) and a professor of radiation...
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