Regina Barzilay, faculty lead for artificial intelligence at MIT Jameel Clinic, Adam Yala, former research affiliate at the MIT Jameel Clinic, and their colleagues have developed an AI-assisted risk prediction model for breast cancer, known as Mirai, which is trained on a large dataset of mammography images and information from Massachusetts General Hospital.
مقتطفات
Titles and Affiliations
Professor, Electrical Engineering and Computer Science
School of Engineering Distinguished Professor for AI and Health
Faculty Co-Leader, Jameel Clinic-MIT Initiative in Machine Learning and Health
Research area
Developing targeted screening strategies to improve breast cancer risk prediction.
Impact
Risk prediction models can inform the use of targeted screening strategies to achieve earlier detection of breast cancer. To improve existing risk prediction models, Drs. Barzilay, Yala, and their colleagues have developed a mammography-based deep learning model called MIRAI. This model was designed to predict an individual’s risk of developing breast cancer by analyzing multiple mammography timepoints and leveraging potentially missing risk factor information. In addition, MIRAI has the potential to produce breast cancer risk predictions that are consistent across mammography machines. Developing MIRAI involved utilizing a large dataset of mammographic information from Massachusetts General Hospital (MGH). It was tested across diverse patient data accumulated from seven hospitals in the United States and other countries. In this retrospective testing, the model was able to reliably identify high-risk patients, outperforming existing methods by a large margin. The goal of the current study is to test MIRAI in a prospective study, where patients predicted to be high-risk by this model are followed by MRI screening to assess its accuracy.