Key result
Fully automatic deep learning achieves coronary artery calcium scores comparable to manual quantification.
Why the study?
To develop a fully automatic multiview shape constraint framework for comprehensive coronary artery calcium scores quantification via deep learning on nonenhanced cardiac CT images.
Does a fully automatic deep learning framework accurately quantify coronary artery calcium scores compared to manual assessment in non-contrast cardiac CT scans?
Observational (n=232)
No
Does a fully automatic deep learning framework accurately quantify coronary artery calcium scores compared to manual assessment in non-contrast cardiac CT scans?
Absolute Event Rate: 535.3% vs 542%
p-value: p=0.993
A fully automatic deep learning framework can accurately quantify coronary artery calcium scores on non-contrast cardiac CT scans, performing comparably to manual assessment.
Automatic DL CACS quantification matches manual assessment; supports automation but leaves open clinical adoption pending prospective validation.
OBJECTIVES: To develop a fully automatic multiview shape constraint framework for comprehensive coronary artery calcium scores (CACS) quantification via deep learning on nonenhanced cardiac CT images. METHODS: In this retrospective single-centre study, a multi-task deep learning framework was proposed to detect and quantify coronary artery calcification from CT images collected between October 2018 and March 2019. A total of 232 non-contrast cardiac-gated CT scans were retrieved and studied (80 % for model training and 20 % for testing). CACS results of testing datasets (n = 46), including Agatston score, calcium volume score, calcium mass score, were calculated fully automatically and manually at total and vessel-specific levels, respectively. RESULTS: No significant differences were found in CACS quantification obtained using automatic or manual methods at total and vessel-specific levels (Agatston score: automatic 535.3 vs. manual 542.0, P = 0.993; calcium volume score: automatic 454.2 vs. manual 460.6, P = 0.990; calcium mass score: automatic 128.9 vs. manual 129.5, P = 0.992). Compared to the ground truth, the number of calcified vessels can be accurate recognized automatically (total: automatic 107 vs. manual 102, P = 0.125; left main artery: automatic 15 vs. manual 14, P = 1.000 ; left ascending artery: automatic 37 vs. manual 37, P = 1.000; left circumflex artery: automatic 22 vs. manual 20, P = 0.625; right coronary artery: automatic 33 vs. manual 31, P = 0.500). At the patient's level, there was no statistic difference existed in the classification of Agatston scoring (P = 0.317) and the number of calcified vessels (P = 0.102) between the automatic and manual results. CONCLUSIONS: The proposed framework can achieve reliable and comprehensive quantification for the CACS, including the calcified extent and distribution indicators at both total and vessel-specific levels.
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Zhang et al. (2020) conducted an observational in Coronary artery calcification (n=232). Automatic deep learning framework for CACS quantification vs. Manual quantification was evaluated on Agatston score (p=0.993). A fully automatic deep learning framework achieved coronary artery calcium scores comparable to manual quantification (Agatston score 535.3 vs 542.0, P=0.993).
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