Deep learning AI improves interreader agreement in CAD-RADS assessments on coronary CT angiography.
Key Details
- 1Study published in Radiology: Cardiothoracic Imaging (Oct 9).
- 2623 patients (11,214 coronary segments) who underwent CCTA were included.
- 3AI-assisted CAD-RADS readings showed higher agreement: 1 vs. 2 readers (manual: 73.7%, AI-assisted: 81.8%); 1 vs. 3 (manual: 77.9%, AI-assisted: 84.5%).
- 4AI algorithm evaluated lesion, segment, and patient levels for CAD-RADS and classified stenosis grades with visual outputs.
- 5Authors call for further research with larger datasets and diverse settings.
Why It Matters
Standardizing CAD-RADS assessments using AI has the potential to reduce variability and errors in coronary CT reporting, which could improve patient management and facilitate training for less experienced readers.
Source
AuntMinnie
Related News
•AuntMinnie
Study: Computer Vision Models Best LLMs in Chest CT Breast Abnormality Detection
Computer vision models (CVMs) surpass large language models (LLMs) in accurately labeling incidental breast abnormalities on chest CT scans.
•Cardiovascular Business
Radiology Maintains Lead in FDA-Cleared AI Algorithms, Cardiology Follows
Radiology remains the top specialty for FDA-cleared AI, with cardiology as a strong second, particularly in cardiovascular imaging.
•AuntMinnie
Deep Learning Models Rival Radiologists for Pancreatic Cancer Detection on CT
Deep-learning models achieved comparable or superior accuracy to experienced radiologists in detecting pancreatic cancer on CT scans, especially for small tumors.