A large study found that AI-based computer-aided detection (AI-CAD) can improve breast cancer detection and support radiologists in screening mammography.
Key Details
- 1The study analyzed data from 24,543 women undergoing screening mammography between 2021 and 2022.
- 2AI-CAD (Lunit Insight Mammography v1.1.7.1) achieved a sensitivity of 89.9% and specificity of 94.3%.
- 3The positive predictive value of recall (PPV1) was 8.7%, slightly above BI-RADS benchmarks.
- 4AI-CAD found 3.4% of cancers missed by radiologists, but missed 8.1% detected by radiologists.
- 5AI-CAD false negatives were more common in women with dense breast tissue, with an overall false negative rate of 10.1%.
- 6AI-CAD does not incorporate prior mammograms unlike human readers.
Why It Matters
These results affirm that AI-CAD can enhance radiologists' performance in breast cancer screening, potentially improving early detection rates. Understanding strengths and limitations of AI-CAD helps guide its integration into clinical workflows, especially considering variable performance in dense breast tissue.
Source
AuntMinnie
Related News
•AuntMinnie
Vast Majority of FDA-Cleared Radiology AI Devices Lack Outcome Trials
Only 0.3% of FDA-cleared radiology AI devices have registered trials on patient outcomes, raising concerns about evidence standards.
•AuntMinnie
AI Framework Boosts MRI Quality Control for Prostate Cancer
A deep learning AI framework improved radiologist and model accuracy for prostate cancer detection by assessing MRI image quality.
•Cardiovascular Business
AI Finds Hidden Aortic Stenosis in Routine Chest CTs
AI algorithms can identify undiagnosed aortic stenosis on routine chest CT scans, potentially improving early detection and patient care.