AI-assisted technique offers effective and painless breast imaging alternative

A Caltech-led team has developed a safe, effective, and painless breast imaging technique that incorporates machine learning to help differentiate between suspicious and healthy tissue.

For decades, X-ray mammography has been the gold standard of breast imaging for the early detection of breast cancer. While the technique remains valuable in terms of reducing cancer deaths, it does expose patients to small amounts of ionizing radiation, painfully squeezes breasts to allow X-rays to more easily pass through tissue, and, especially in the case of dense breast tissue, produces many false positive diagnoses.

Other techniques such as ultrasound and magnetic resonance imaging (MRI) can be used for breast imaging, but these also have problems. Ultrasound is very safe, but its accuracy is dependent on the skill of the operator and the results are not always conclusive. MRI is time-intensive, expensive, and cannot be used on patients who are allergic to contrast agents or those who are claustrophobic or have certain implants.

“We were strongly motivated to work on this problem because none of the current techniques are perfect,” says Lihong Wang, the Bren Professor of Medical Engineering and Electrical Engineering at Caltech. “The future of medicine has to be better than that.”

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