The Mirai AI model significantly improves detection of interval breast cancers in negative screening mammograms.
Key Details
- 1Mirai risk model analyzed 134,217 screening mammograms, including 524 interval cancers.
- 2Top 20% risk group by Mirai captured 42.4% of interval cancers, corresponding to 1.7 additional detections per 1,000 exams.
- 3AUC values for interval cancer prediction ranged from 0.67 to 0.72 across time, age, and breast density subgroups.
- 4No significant performance variation across different age groups or breast densities was observed.
- 5Mirai has been validated on nearly 2 million mammograms across 21 countries.
- 6Editorial comments highlight progress but note limitations since interval cancer detection did not surpass 50%.
Why It Matters
Interval cancers are a challenge in breast screening, often missed during routine exams and harder to detect. AI-driven risk models like Mirai offer a promising path to personalized screening strategies, potentially catching cancers earlier and improving patient outcomes.
Source
AuntMinnie
Related News
•AuntMinnie
AI Advancements and Studies Highlighted in Digital X-Ray Insider
This edition covers AI models for fracture detection, mortality prediction, and more, along with new research using x-ray and DEXA modalities.
•AuntMinnie
AI Model Accurately Detects Pediatric Physeal Fractures on X-Ray
A deep learning model accurately identifies hard-to-detect physeal fractures in children's wrist x-rays.
•AuntMinnie
Adult-Trained Radiology AI Models Struggle in Pediatric Imaging
Adult-trained radiology AI models often underperform when applied to pediatric imaging data, according to a systematic review.