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
•AuntMinnie
AI Outperforms Physicians in Detecting Achalasia on Chest X-Rays
An AI model achieved high accuracy in identifying esophageal achalasia on chest x-rays, surpassing physician performance.
•AuntMinnie
AI Uses Mammograms to Predict Women's Cardiovascular Risk
A deep learning AI model based on mammographic features can predict cardiovascular risk in women with accuracy comparable to traditional risk scores.
•Health Imaging
Are Chatbots Maturing for Radiology Use? Experts Assess LLM Progress
Recent research evaluates improvements in large language models for radiology tasks, revealing both progress and ongoing limitations.