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Purpose <p>To validate a fully automated, deep learning model based on coronary computed tomography angiography (CCTA) for the diagnosis of obstructive coronary artery disease (CAD) with stenosis ≥ 50%, which is commonly used as a clinical threshold for further testing and management. This model aims to improve diagnostic efficiency by automating the identification of significant coronary stenosis(≥ 50%).</p> Methods <p>This multicenter clinical trial included patients been undergone CCTA from October 13, 2022, to February 28, 2023. CCTA data from suspected coronary artery disease (CAD) patients were retrospectively analyzed using deep learning-based software for comprehensive assessment, including coronary segmentation, lumen, and stenosis determination with comparison to the reference standard of consensus by three experts. This study utilized a multi-stage deep learning framework for coronary artery segmentation and stenosis analysis from CCTA images, consisting of several key components, including the 3D Multi-resolution Cascade Convolutional Neural Network (CNN), 3D Cascade-Locally Optimized Network, and Stenosis Analysis Network. The clinical trial registry number was NCT06172985.</p> Results <p>A total of 1090 patients (mean age: 59.90 ± 11.51 years, 47.3% female) were included in this multicenter study. Artificial intelligence (AI) demonstrated excellent performance at the patient level, accurately diagnosing ≥ 50% stenosis by assessing each patient’s coronary artery condition. The AI system showed high values for accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The values of the above statistics were 92.8%, 95.3%, 91.4%, 85.6%, and 97.3%, respectively. Excellent agreement was seen between expert readers and deep learning-determined maximal diameter stenosis for per-patient (kappa coefficients: 0.84, 95%CI: 0.81–0.88). Regarding diagnostic efficiency, comparing the AI with expert readers, the average reading time decreased from 5.94&#xa0;min to 2.01&#xa0;min (<i>p</i> &lt; 0.001).</p> Conclusion <p>A novel AI-based assessment of CCTA can accurately and rapidly identify patients with coronary artery stenosis ≥ 50%, aiding in effective triage within the defined study population.</p>

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Diagnostic performance of deep learning-based coronary computed tomography angiography in detecting coronary artery stenosis

  • 期刊论文

  • 发表日期：

    2025年3月29日

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  • Yang Chen,
  • Hong Yu,
  • Bin Fan,
  • Yong Wang,
  • Zhibo Wen,
  • Zhihui Hou,
  • Jihong Yu,
  • Haiping Wang,
  • Zhe Tang,
  • Ning Li,
  • Peng Jiang,
  • Yang Wang,
  • Weihua Yin,
  • Bin Lu

摘要

Purpose

To validate a fully automated, deep learning model based on coronary computed tomography angiography (CCTA) for the diagnosis of obstructive coronary artery disease (CAD) with stenosis ≥ 50%, which is commonly used as a clinical threshold for further testing and management. This model aims to improve diagnostic efficiency by automating the identification of significant coronary stenosis(≥ 50%).

Methods

This multicenter clinical trial included patients been undergone CCTA from October 13, 2022, to February 28, 2023. CCTA data from suspected coronary artery disease (CAD) patients were retrospectively analyzed using deep learning-based software for comprehensive assessment, including coronary segmentation, lumen, and stenosis determination with comparison to the reference standard of consensus by three experts. This study utilized a multi-stage deep learning framework for coronary artery segmentation and stenosis analysis from CCTA images, consisting of several key components, including the 3D Multi-resolution Cascade Convolutional Neural Network (CNN), 3D Cascade-Locally Optimized Network, and Stenosis Analysis Network. The clinical trial registry number was NCT06172985.

Results

A total of 1090 patients (mean age: 59.90 ± 11.51 years, 47.3% female) were included in this multicenter study. Artificial intelligence (AI) demonstrated excellent performance at the patient level, accurately diagnosing ≥ 50% stenosis by assessing each patient’s coronary artery condition. The AI system showed high values for accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The values of the above statistics were 92.8%, 95.3%, 91.4%, 85.6%, and 97.3%, respectively. Excellent agreement was seen between expert readers and deep learning-determined maximal diameter stenosis for per-patient (kappa coefficients: 0.84, 95%CI: 0.81–0.88). Regarding diagnostic efficiency, comparing the AI with expert readers, the average reading time decreased from 5.94 min to 2.01 min (p < 0.001).

Conclusion

A novel AI-based assessment of CCTA can accurately and rapidly identify patients with coronary artery stenosis ≥ 50%, aiding in effective triage within the defined study population.