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Clinical Validation of a Deep Learning Algorithm for Automated Coronary Artery Disease Detection and Classification
Emanuele Muscogiuri1,2, Marly van Assen1, Giovanni Tessarin1,3,4
1Division of Cardiothoracic Imaging, Department of Radiology and Imaging Sciences.
Insights
A deep learning algorithm accurately detects coronary artery disease (CAD) and obstructive CAD using cardiac CT angiography. This automated tool shows excellent agreement with expert readers and significantly reduces analysis time.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Imaging
Background:
- Coronary artery disease (CAD) is a leading cause of mortality.
- Cardiac computed tomography angiography (CCTA) is a key imaging modality for CAD detection.
- Automated analysis tools are needed to improve efficiency and accuracy in CCTA interpretation.
Purpose of the Study:
- To clinically validate a deep learning (DL) algorithm for CAD detection and classification.
- To assess the performance of the DL algorithm on a heterogeneous multivendor CCTA dataset.
- To compare the DL algorithm's performance against expert readers and radiologic reports.
Main Methods:
- Retrospective single-center study including 296 patients with CCTA scans from 4 vendors.
- Coronary Artery Disease-Reporting and Data System (CAD-RADS) classification by DL algorithm and expert readers.
- Variability analysis using a second reader and radiology reports.
- Statistical analysis stratified by CAD presence and obstructive CAD.
Main Results:
- DL algorithm achieved 95.3% sensitivity and 87.5% accuracy for CAD detection.
- DL algorithm achieved 89.4% sensitivity and 92.2% accuracy for obstructive CAD detection.
- DL algorithm demonstrated excellent agreement with expert readers and significantly reduced analysis time (P < 0.001).
Conclusions:
- The DL algorithm shows robust performance in evaluating CAD from CCTA.
- The algorithm demonstrates excellent agreement with expert readers.
- Automated DL analysis of CCTA for CAD offers a significant reduction in image analysis time.
Purpose:
We sought to clinically validate a fully automated deep learning (DL) algorithm for coronary artery disease (CAD) detection and classification in a heterogeneous multivendor cardiac computed tomography angiography data set.
Materials And Methods:
In this single-centre retrospective study, we included patients who underwent cardiac computed tomography angiography scans between 2010 and 2020 with scanners from 4 vendors (Siemens Healthineers, Philips, General Electrics, and Canon). Coronary Artery Disease-Reporting and Data System (CAD-RADS) classification was performed by a DL algorithm and by an expert reader (reader 1, R1), the gold standard. Variability analysis was performed with a second reader (reader 2, R2) and the radiologic reports on a subset of cases. Statistical analysis was performed stratifying patients according to the presence of CAD (CAD-RADS >0) and obstructive CAD (CAD-RADS ≥3).
Results:
Two hundred ninety-six patients (average age: 53.66 ± 13.65, 169 males) were enrolled. For the detection of CAD only, the DL algorithm showed sensitivity, specificity, accuracy, and area under the curve of 95.3%, 79.7%, 87.5%, and 87.5%, respectively. For the detection of obstructive CAD, the DL algorithm showed sensitivity, specificity, accuracy, and area under the curve of 89.4%, 92.8%, 92.2%, and 91.1%, respectively. The variability analysis for the detection of obstructive CAD showed an accuracy of 92.5% comparing the DL algorithm with R1, and 96.2% comparing R1 with R2 and radiology reports. The time of analysis was lower using the DL algorithm compared with R1 ( P < 0.001).
Conclusions:
The DL algorithm demonstrated robust performance and excellent agreement with the expert readers' analysis for the evaluation of CAD, which also corresponded with significantly reduced image analysis time.
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