Artificial intelligence-enabled comprehensive coronary phenotyping in patients with suspected CAD
Bibliographic record
Abstract
Abstract Introduction The capabilities of artificial intelligence (AI) are rapidly progressing and the research community is getting increasingly interested in its possibilities. AI algorithms are able to work continuously and at high speed, reducing human workload and saving time that physicians can spend on more complex data or rarer cases. However, many clinical AI applications are currently only used in a research setting and lack proper testing and validation. Objectives This study aimed to determine the accuracy and performance of a novel AI-based software tool for CCTA analysis compared to conventional expert evaluation. Methods We evaluated 100 CCTA exams from a cohort of symptomatic patients with mild-to-moderately abnormal non-invasive ischemia test. Stenosis severity assessed by AI-based analysis (automatic evaluation, AEv) was compared with a level III expert CCTA interpretation (manual evaluation, MEv). AI-based analysis reported exact % stenosis and obstructive CAD was considered if maximal stenosis was ≥50%. Plaque phenotype was also estimated using AI algorithms. Results The study cohort was as follows: 52% male, mean age 68±10 years. The prevalence of hypertension, dyslipidemia and diabetes was 77%, 81% and 23%, respectively, and 10-year cardiovascular risk was 19±10% as predicted by Framingham risk score. Typical angina was present in 33%, of which 67% had a Canadian Cardiovascular Society angina grade ≥2. Overall prevalence of obstructive CAD determined by MEv and AEv was 25% and 21%, respectively, with a significant association between both assessments (p<0.001). When compared to MEv as reference, AEv method performed with a sensitivity, specificity, positive and negative predictive values of 0.56, 0.91, 0.58 and 0.86, respectively. Area under the curve was 0.871 (p<0.001) demonstrating high accuracy. AEv atherosclerosis quantification revealed significant differences between patients with and without obstructive CAD according to MEv: median total plaque volume (569 vs 115 mm3, p<0.001), calcified plaque volume (297 vs 19 mm3, p<0.001), non-calcified plaque volume (235 vs 71 mm, p<0.001), low-density non-calcified plaque volume (2.8 vs 1.0 mm3, p=0.023) and percent atheroma volume (16.1 vs 3.8 mm3, p<0.001). Conclusion In patients with suspected CAD and mild-to-moderately abnormal ischemia tests, a diagnostic strategy using AEv as a gatekeeper is effective, providing a quantitative stenosis evaluation with similar diagnostic performance for obstructive CAD when compared to MEv. AI-enabled approach additionally allows a fully automated quantification of coronary plaque volumes and composition, which would further enhance risk stratification. Funding Acknowledgement Type of funding sources: None.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".