A study finds mammography acquisition settings influence both AI and radiologist performance in breast cancer detection.
Key Details
- 1Seven acquisition parameters studied: machine type, kVp, x-ray exposure, relative exposure, paddle size, compression force, tissue thickness.
- 2Dataset: 28,278 2D mammograms from 22,626 women; 324 with cancer diagnosis within a year.
- 3Radiologists: Sensitivity 79.3%, specificity 88.7%; AI: Sensitivity 76.9%, specificity 76.9%.
- 4Increased x-ray exposure reduced specificity for AI but not radiologists; increased compression reduced specificity for radiologists but not AI.
- 5Trends for kVp: little effect on sensitivity, slight increase in specificity for both AI and radiologists.
Why It Matters
Understanding how technical factors affect AI versus human interpretation is vital for optimizing AI deployment in mammography. These insights could inform guidelines, quality control, and trust calibration when integrating AI into clinical screening workflows.
Source
AuntMinnie
Related News
•Radiology Business
RadNet Study: AI Boosts Breast Cancer Detection in Largest-Ever Real-World Analysis
A massive real-world study by RadNet shows AI-assisted mammography increased breast cancer detection by 21.6%.
•AuntMinnie
Multimodal MRI Radiomics Model Predicts Long-Term Survival in Breast Cancer
A multimodal MRI radiomics and deep learning model outperformed traditional models in predicting 5- and 7-year survival for breast cancer patients receiving neoadjuvant chemotherapy.
•AuntMinnie
AI Predicts 10-Year Mortality and Hip Fracture Risk from DEXA Scans
A self-supervised AI model predicts 10-year mortality and hip fractures using only DEXA scans.