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<p>Quantifying aortic valve calcification is critical for assessing the severity of aortic stenosis, predicting cardiovascular risk, and guiding treatment decisions. This study evaluated the feasibility of a deep learning-based automatic quantification of aortic valve calcification using contrast-enhanced coronary CT angiography and compared the results with manual calcium scoring. A retrospective analysis of 177 patients undergoing aortic stenosis evaluation was conducted, divided into a development set (<i>n</i> = 97) and an internal validation set (<i>n</i> = 80). The DeepLab v3 + model segmented the ascending aorta, and the XGBoost model refined the aortic valve region using representative attenuation values. Calcifications were identified with a tailored threshold based on these values and quantified using a weighted scoring method analogous to the Agatston score. The automated method showed excellent agreement with manual Agatston scores derived from non-contrast CT (Pearson correlation coefficient = 0.93, 95% confidence interval [CI]: 0.89–0.95, <i>p</i> &lt; 0.001, concordance correlation coefficient = 0.92, 95% CI: 0.87–0.95). For classifying severe aortic stenosis, defined by calcium scores exceeding 2000 for men and 1300 for women, the approach achieved a sensitivity of 88.6%, specificity of 91.1%, and overall accuracy of 90.0%. This deep learning model provides automated aortic valve calcification quantification with high accuracy on enhanced CT. This approach offers an alternative for measuring aortic valve calcium when non-contrast CT is unavailable, with the potential to reduce reliance on non-contrast CT, minimize operator dependency, and lower patient radiation exposure.</p>

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Deep learning based automatic quantification of aortic valve calcification on contrast enhanced coronary CT angiography

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月12日

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  • Daebeom Park,
  • Soon-Sung Kwon,
  • Yoona Song,
  • Yoon A Kim,
  • Baren Jeong,
  • Whal Lee,
  • Eun-Ah Park

摘要

Quantifying aortic valve calcification is critical for assessing the severity of aortic stenosis, predicting cardiovascular risk, and guiding treatment decisions. This study evaluated the feasibility of a deep learning-based automatic quantification of aortic valve calcification using contrast-enhanced coronary CT angiography and compared the results with manual calcium scoring. A retrospective analysis of 177 patients undergoing aortic stenosis evaluation was conducted, divided into a development set (n = 97) and an internal validation set (n = 80). The DeepLab v3 + model segmented the ascending aorta, and the XGBoost model refined the aortic valve region using representative attenuation values. Calcifications were identified with a tailored threshold based on these values and quantified using a weighted scoring method analogous to the Agatston score. The automated method showed excellent agreement with manual Agatston scores derived from non-contrast CT (Pearson correlation coefficient = 0.93, 95% confidence interval [CI]: 0.89–0.95, p < 0.001, concordance correlation coefficient = 0.92, 95% CI: 0.87–0.95). For classifying severe aortic stenosis, defined by calcium scores exceeding 2000 for men and 1300 for women, the approach achieved a sensitivity of 88.6%, specificity of 91.1%, and overall accuracy of 90.0%. This deep learning model provides automated aortic valve calcification quantification with high accuracy on enhanced CT. This approach offers an alternative for measuring aortic valve calcium when non-contrast CT is unavailable, with the potential to reduce reliance on non-contrast CT, minimize operator dependency, and lower patient radiation exposure.