Key People & Companies
NYU Langone Health
Deep Learning AI System for Breast Cancer MRI
NYU Langone Health · Internal
Krzysztof J. Geras
Assistant Professor, Department of Radiology at NYU Grossman School of Medicine / NYU Langone Health
Jan Witowski
Postdoctoral Fellow, MD, PhD at NYU Langone Health / NYU School of Medicine
Linda Moy
Professor of Radiology at NYU Langone Health
DCE-MRI
Imaging_technology
BI-RADS 4
American College of Radiology · diagnostic_category
3D-ResNet18
NYU Langone Health (custom implementation) · Framework
+ 8 more entities in the full study
Key Results
- The AI system achieved an AUROC of 0.924 (95% CI: 0.915-0.933) on NYU Langone's internal test set of 3,936 MRI exams, statistically equivalent to the average performance of five board-certified breast radiologists (AUROC 0.890, range 0.850-0.948).
- External validation demonstrated generalizability: AUROC of 0.788 on 397 studies from Jagiellonian University Hospital in Poland, and 0.969 on 922 studies from Duke University.
- Hybrid predictions—averaging AI scores with radiologist assessments—improved AUROC by 0.05 and AUPRC by 0.07 compared to radiologists alone, while boosting interreader agreement from a Fleiss' kappa of 0.56 to 0.77.
- + 3 more results inside
“Breast MRI exams are difficult and time-consuming to interpret, even for experienced radiologists. AI has tremendous potential in improving medical diagnosis, as it can learn from tens of thousands of exams. Using AI to assist radiologists can make the process more accurate, and provide a higher level of confidence in the results.”
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