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Updated: Oct 2, 2025
In Vivo Quantitative Assessment of Myocardial Structure, Function, Perfusion and Viability Using Cardiac Micro-computed Tomography
Published on: February 16, 2016
Diagnostic performance of deep learning algorithm for analysis of computed tomography myocardial perfusion
Giuseppe Muscogiuri1, Mattia Chiesa1,2, Andrea Baggiano1
1Centro Cardiologico Monzino, IRCCS, Milan, Italy.
Insights
A deep learning algorithm accurately predicts coronary artery disease (CAD) using rest myocardial computed tomography perfusion (CTP) scans. This method is faster than traditional analysis and may serve as a pre-screening tool before stress CTP.
Area of Science:
- Cardiovascular imaging
- Artificial intelligence in medicine
- Diagnostic accuracy studies
Background:
- Coronary artery disease (CAD) diagnosis often relies on invasive procedures.
- Non-invasive methods like computed tomography perfusion (CTP) are evolving.
- Deep learning (DL) offers potential for improving diagnostic efficiency and accuracy.
Purpose of the Study:
- To assess the diagnostic accuracy of a DL algorithm for predicting hemodynamically significant CAD.
- To evaluate the DL algorithm's performance using rest myocardial CTP datasets.
- To compare the DL algorithm's accuracy and speed against traditional CTP stress tests and invasive evaluation.
Main Methods:
- One hundred and twelve symptomatic patients underwent CTP and invasive coronary angiography (ICA) with fractional flow reserve (FFR).
- A DL algorithm (CTP-DLrest and CTP-DLstress) was developed to predict significant CAD.
- Diagnostic accuracy metrics (sensitivity, specificity, AUC) were compared for CCTA alone, CCTA + CTP stress, CCTA + CTP-DLrest, and CCTA + CTP-DLstress.
Main Results:
- DL algorithms (CTP-DLrest and CTP-DLstress) significantly improved CAD detection compared to CCTA alone (p < 0.01).
- CTP-DLrest achieved high accuracy (84%) and AUC (96%), while CTP-DLstress showed 88% accuracy and 98% AUC.
- DL analysis time was substantially lower (39.2s) than human analysis (379.6s), p < 0.001.
Conclusions:
- Deep learning evaluation of myocardial ischemia using rest CTP datasets is feasible and accurate.
- The DL approach demonstrates potential as an efficient gatekeeper before CTP stress imaging.
- This AI-driven method may streamline the diagnostic pathway for significant coronary artery disease.
Purpose:
To evaluate the diagnostic accuracy of a deep learning (DL) algorithm predicting hemodynamically significant coronary artery disease (CAD) by using a rest dataset of myocardial computed tomography perfusion (CTP) as compared to invasive evaluation.
Methods:
One hundred and twelve consecutive symptomatic patients scheduled for clinically indicated invasive coronary angiography (ICA) underwent CCTA plus static stress CTP and ICA with invasive fractional flow reserve (FFR) for stenoses ranging between 30 and 80%. Subsequently, a DL algorithm for the prediction of significant CAD by using the rest dataset (CTP-DLrest) and stress dataset (CTP-DLstress) was developed. The diagnostic accuracy for identification of significant CAD using CCTA, CCTA + CTP stress, CCTA + CTP-DLrest, and CCTA + CTP-DLstress was measured and compared. The time of analysis for CTP stress, CTP-DLrest, and CTP-DLStress was recorded.
Results:
Patient-specific sensitivity, specificity, NPV, PPV, accuracy, and area under the curve (AUC) of CCTA alone and CCTA + CTPStress were 100%, 33%, 100%, 54%, 63%, 67% and 86%, 89%, 89%, 86%, 88%, 87%, respectively. Patient-specific sensitivity, specificity, NPV, PPV, accuracy, and AUC of CCTA + DLrest and CCTA + DLstress were 100%, 72%, 100%, 74%, 84%, 96% and 93%, 83%, 94%, 81%, 88%, 98%, respectively. All CCTA + CTP stress, CCTA + CTP-DLRest, and CCTA + CTP-DLStress significantly improved detection of hemodynamically significant CAD compared to CCTA alone (p < 0.01). Time of CTP-DL was significantly lower as compared to human analysis (39.2 ± 3.2 vs. 379.6 ± 68.0 s, p < 0.001).
Conclusion:
Evaluation of myocardial ischemia using a DL approach on rest CTP datasets is feasible and accurate. This approach may be a useful gatekeeper prior to CTP stress..
