Automated coronary artery calcium scoring using nested U-Net and focal loss

Jia Sheng Hong, Yun Hsuan Tzeng, Wei Hsian Yin, Kuan Ting Wu, Huan Yu Hsu, Chia Feng Lu, Ho Ren Liu*, Yu Te Wu


研究成果: Article同行評審

4 引文 斯高帕斯(Scopus)


Coronary artery calcium (CAC) is a great risk predictor of the atherosclerotic cardiovascular disease and CAC scores can be used to stratify the risk of heart disease. Current clinical analysis of CAC is performed using onsite semiautomated software. This semiautomated CAC analysis requires experienced radiologists and radiologic technologists and is both demanding and time-consuming. The purpose of this study is to develop a fully automated CAC detection model that can quantify CAC scores. A total of 1,811 cases of cardiac examinations involving contrast-free multidetector computed tomography were retrospectively collected. We divided the database into the Training Data Set, Validation Data Set, Testing Data Set 1, and Testing Data Set 2. The Training, Validation, and Testing Data Set 1 contained cases with clinically detected CAC; Testing Data Set 2 contained those without detected calcium. The intraclass correlation coefficients between the overall standard and model-predicted scores were 1.00 for both the Training Data Set and Testing Data Set 1. In Testing Data Set 2, the model was able to detect clinically undetected cases of mild calcium. The results suggested that the proposed model's automated detection of CAC was highly consistent with clinical semiautomated CAC analysis. The proposed model demonstrated potential for clinical applications that can improve the quality of CAC risk stratification.

頁(從 - 到)1681-1690
期刊Computational and Structural Biotechnology Journal
出版狀態Published - 1月 2022


深入研究「Automated coronary artery calcium scoring using nested U-Net and focal loss」主題。共同形成了獨特的指紋。