Because more than 90% of women lack a known genetic predisposition to or a family history of breast cancer, this innovative approach could fill a critical gap in risk assessment and save countless lives.

"For women with a known genetic risk factor for breast cancer, there are things you can do like follow a higher-risk screening protocol. For everybody else, you're left wondering, 'Am I at high risk?'" said Mark LaBarge, Ph.D., a professor in the Department of Population Sciences at City of Hope.

"By translating physical changes in cells into quantifiable data, this tool gives women something tangible to discuss with their doctors—not just risk estimates, but evidence drawn directly from their own cells."

Researchers from the two institutions developed a machine learning algorithm that identifies and measures cells that show signs of accelerated aging, quantifying an individual breast cancer risk score. Importantly, the AI platform uses simple electronics that would be easy and affordable to replicate on a large scale.

"Our team isn't the first to measure the mechanical properties of cells; however, other approaches require advanced imaging technology that's expensive, cumbersome, and has limited availability," said Lydia Sohn, Ph.D., the Almy C. Maynard and Agnes Offield Maynard Chair in Mechanical Engineering at UC Berkeley.