AI algorithms can identify undiagnosed aortic stenosis on routine chest CT scans, potentially improving early detection and patient care.
Key Details
- 1Researchers at Cedars-Sinai tested AI to flag aortic stenosis on 440 routine non-contrast, nongated chest CT scans of adults over 50.
- 2The AI, developed by Nanox, found aortic valve calcium in 178 patients; 84.8% of these findings were not reported in original radiology reports.
- 3Only 1.4% of patients (6/440) met the threshold for severe aortic stenosis, but 22.5% (99/440) had moderately high risk (≥100 Agatston units).
- 4Aortic valve calcium was measured using the Agatston scoring method, the same as in CT coronary calcium scoring.
- 5Earlier detection could redirect patients into appropriate surveillance and intervention pathways, addressing gaps in current care.
Why It Matters
This study highlights AI's potential to serve as an early, opportunistic screening tool for heart disease on scans performed for other reasons, reducing missed diagnoses and improving patient outcomes through prompt intervention.
Source
Cardiovascular Business
Related News
•Cardiovascular Business
Best Practices for Integrating AI Into Cardiac Imaging Workflows
Successful AI integration in cardiac imaging requires more than just software installation, emphasizing the need for clinician leadership and multidisciplinary coordination.
•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.