Better breast cancer diagnosis through machine-learning ultrasound

Mammography is the gold standard for breast cancer diagnosis, but it’s not reliably accurate in all cases, especially in people with dense breasts. Avice O’Connell, a professor of imaging sciences, Kevin Parker, a professor of electrical and computer engineering, and Jihye Baek, a PhD student in electrical and computer engineering, have launched a research project incorporating ultrasound with machine learning for previously detected masses. The end result: nearly 98 percent accuracy in predicting breast cancer in these masses.

@Rochester 4.21.23