Diagnostic performance of deep learning-based vascular extraction and stenosis detection technique for coronary
Meng Chen1,2, Ximing Wang1,2, Guangyu Hao1,2
1Department of Radiology, The First Affiliated Hospital of Soochow University, NO.899 Pinghai Road, Gusu District, Suzhou, Jiangsu, China.
Insights
Deep learning (DL) technology accurately detects coronary artery disease (CAD) with high sensitivity and specificity. This advanced tool significantly reduces analysis time compared to traditional methods, offering a promising diagnostic solution.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Coronary artery disease (CAD) is a leading cause of mortality worldwide.
- Accurate and efficient diagnostic tools are crucial for timely intervention.
- Current diagnostic methods for CAD can be time-consuming and resource-intensive.
Purpose of the Study:
- To evaluate the diagnostic performance of deep learning (DL) based technology for coronary artery disease (CAD) detection.
- To compare the efficacy of DL technology against human readers in assessing coronary stenosis.
- To assess the time efficiency of DL technology in CAD analysis.
Main Methods:
- Retrospective analysis of coronary computed tomography angiography (CCTA) in 124 patients.
- Invasive coronary angiography used as the reference standard.
- Evaluation of DL model and reader model performance at patient, vessel, and segment levels using Area Under the Curve (AUC).
Main Results:
- DL model achieved an AUC of 0.78 for obstructive CAD detection at the patient level (sensitivity 94%, specificity 63%).
- DL model demonstrated comparable or superior performance to human readers in vessel and segment-level analyses.
- DL analysis time was significantly reduced to 0.47 minutes per patient compared to 29.65 minutes for human readers (p < 0.001).
Conclusions:
- Deep learning technology shows high accuracy and effectiveness in identifying obstructive CAD.
- DL technology offers a faster and potentially more reliable diagnostic tool for CAD assessment.
- The study highlights the valuable prospect of DL in improving CAD diagnosis.
Objective:
To investigate the diagnostic performance of deep learning (DL)-based vascular extraction and stenosis detection technology in assessing coronary artery disease (CAD).
Methods:
The diagnostic performance of DL technology was evaluated by retrospective analysis of coronary computed tomography angiography in 124 suspected CAD patients, using invasive coronary angiography as reference standard. Lumen diameter stenosis ≥50% was considered obstructive, and the diagnostic performances were evaluated at per-patient, per-vessel and per-segment levels. The diagnostic performances between DL model and reader model were compared by the areas under the receiver operating characteristics curves (AUCs).
Results:
In patient-based analysis, AUC of 0.78 was obtained by DL model to detect obstructive CAD [sensitivity of 94%, specificity of 63%, positive predictive value of 94%, and negative predictive value of 59%], While AUC by reader model was 0.74 (sensitivity of 97%, specificity of 50%, positive predictive value of 93%, negative predictive value of 73%). In vessel-based analysis, the AUCs of DL model and reader model were 0.87 and 0.89 respectively. In segment-based analysis, the AUCs of 0.84 and 0.89 were obtained by DL model and reader model respectively. It took 0.47 min to analyze all segments per patient by DL model, which is significantly less than reader model (29.65 min) (p < 0.001).
Conclusion:
The DL technology can accurately and effectively identify obstructive CAD, with less time-consuming, and it could be a reliable diagnostic tool to detect CAD.
Advances In Knowledge:
The DL technology has valuable prospect with the diagnostic ability to detect CAD.
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