Cancer Detection: MIT’s AI System 'Mirai'

Artificial intelligence detects breast cancer 5 years before it develops.

美国麻省理工学院 cutting-edge AI system, Mirai, is setting a new standard in breast cancer detection. Trained on over 200,000 exams from Massachusetts General Hospital (MGH), Mirai has been validated across test sets from MGH, the Karolinska Institute in Sweden, and Chang Gung Memorial Hospital in Taiwan. Now installed at MGH, Mirai is actively being integrated into clinical care, demonstrating remarkable accuracy and predictive power.

Mirai's Breakthrough Performance

Mirai significantly outperforms previous methods, identifying nearly twice as many future cancer diagnoses compared to the Tyrer-Cuzick model. The system is equally accurate across diverse races, age groups, breast density categories, and cancer subtypes. Here’s a closer look at how Mirai works:

Image Aggregator Module:

Purpose: Gathers and processes conventional mammography images to construct a comprehensive mammogram illustration.

Impact: Ensures a full-spectrum view of the mammogram for precise analysis.

Data Aggregation:

Purpose: Integrates image data from all views for thorough examination.

Impact: Enhances the depth and accuracy of image interpretation.

Risk-Factor Prediction Module:

Purpose: Utilizes mammography data to predict individual risk factors.

Impact: Offers a personalized risk profile, guiding targeted interventions.

Additive-Hazard Layer:

Purpose: Combines patient risk variables with mammography analysis to forecast annual risk for the next five years.

Impact: Provides dynamic, ongoing risk assessment to inform timely interventions.

Real-World Impact: Expert Insights        

Adam Yala, the lead author of the study from MIT, highlights Mirai’s capability: "Improved breast cancer risk models enable targeted screening strategies that achieve earlier detection and less screening harm than existing guidelines."

Regina Barzilay, a Professor at MIT, emphasizes Mirai's inclusivity: "Unlike traditional models, our deep learning model performs equally well across diverse races, ages, and family histories."

Enhancing Clinical Guidelines with Mirai

Mirai’s development focused on three critical innovations for risk modeling:

Time:

Predicts risk at all time points simultaneously using an additive-hazard layer, ensuring consistent risk assessments.

Non-image Risk Factors:

Uses predicted values for risk factors if not available, allowing the model to be used globally without specific infrastructure.

Consistent Performance:

Ensures reliable predictions across different clinical settings by learning mammogram representations invariant to the source environment.

Global Validation and Future Improvements

Mirai has shown consistent performance across various clinical settings and populations, including Karolinska in Sweden and Chang Gung Memorial Hospital in Taiwan. Future improvements include utilizing patient imaging history and advanced X-ray techniques like tomosynthesis to further enhance accuracy.

Dr. Judy Gichoya of Emory University notes, "We’re extensively studying how to ensure this AI system works for African-American populations and how to detect failures."

Collaboration and Support

The research team is partnering with clinicians worldwide, including Novant Health, Emory, Maccabi, TecSalud, Apollo, and Barretos, to validate and implement Mirai. The work has been supported by grants from Susan G Komen, Breast Cancer Research Foundation, Quanta Computing, MIT Jameel Clinic, Chang Gung Medical Foundation, and Stockholm L?ns Landsting HMT Grant.

Join the Future of Healthcare        

Mirai’s success marks the dawn of a new era in medical diagnostics. By leveraging deep learning, Mirai offers unparalleled accuracy and personalization, promising improved outcomes for patients globally.

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