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Validation of the commercial coronary computed tomographic angiography artificial intelligence for coronary artery stenosis: a cross-sectional study.

作者信息

Han Qi, Jing Feihua, Sun Zhiguo, Liu Fei, Zhang Jucai, Wang Jian, Liang Hongqin

机构信息

Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China.

Department of Radiology, Linfen Central Hospital, Linfen, China.

出版信息

Quant Imaging Med Surg. 2023 Jun 1;13(6):3789-3801. doi: 10.21037/qims-22-1115. Epub 2023 Apr 12.


DOI:10.21037/qims-22-1115
PMID:37284069
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10240030/
Abstract

BACKGROUND: The commercial coronary computed tomographic angiography artificial intelligence (CCTA-AI) platform has made great progress in clinical application. However, research is needed to elucidate the current stage of commercial AI platforms and the role of radiologists. This study compared the diagnostic performance of the commercial CCTA-AI platform with that of a reader based on a multicenter and multidevice sample. METHODS: A total of 318 patients with suspected coronary artery disease (CAD) who underwent both CCTA and invasive coronary angiography (ICA) were included in a multicenter and multidevice validation cohort between 2017 and 2021. The commercial CCTA-AI platform was used to automatically assess coronary artery stenosis by using ICA findings as the gold standard. The CCTA reader was completed by radiologists. The diagnostic performance of the commercial CCTA-AI platform and CCTA reader was evaluated at the patient and segment levels. The cutoff values of models 1 and 2 were 50% and 70% stenosis, respectively. RESULTS: It took 20.4 seconds to accomplish post-processing per patient when using the CCTA-AI platform, which was significantly shorter than the time taken to complete this task with the CCTA reader (1,112.1 s). In the patient-based analysis, the area under the curve (AUC) was 0.85 using the CCTA-AI platform and 0.61 using the CCTA reader in model 1 (stenosis ratio: 50%). In contrast, the AUC was 0.78 using the CCTA-AI platform and 0.64 using the CCTA reader in model 2 (stenosis ratio: 70%). In the segment-based analysis, the AUCs of CCTA-AI were slightly better than those of the readers. The negative predictive value (NPV) increased from model 1 to model 2. Furthermore, the diagnostic performance was better for larger-diameter arteries. CONCLUSIONS: The commercial CCTA-AI platform may provide a feasible solution for the diagnosis of coronary artery stenosis, and it has a diagnostic performance that is slightly better than that of a radiologist with a moderate level of experience (5-10 years of experience).

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53f1/10240030/0c701712f4fd/qims-13-06-3789-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53f1/10240030/6e62c9b6e6ea/qims-13-06-3789-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53f1/10240030/435027b0f781/qims-13-06-3789-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53f1/10240030/0c701712f4fd/qims-13-06-3789-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53f1/10240030/6e62c9b6e6ea/qims-13-06-3789-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53f1/10240030/435027b0f781/qims-13-06-3789-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53f1/10240030/0c701712f4fd/qims-13-06-3789-f3.jpg

相似文献

[1]
Validation of the commercial coronary computed tomographic angiography artificial intelligence for coronary artery stenosis: a cross-sectional study.

Quant Imaging Med Surg. 2023-6-1

[2]
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[3]
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[4]
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[5]
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[9]
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[10]
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引用本文的文献

[1]
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[2]
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本文引用的文献

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2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain: Executive Summary: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines.

Circulation. 2021-11-30

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Quant Imaging Med Surg. 2021-6

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Evaluation of the performance of traditional machine learning algorithms, convolutional neural network and AutoML Vision in ultrasound breast lesions classification: a comparative study.

Quant Imaging Med Surg. 2021-4

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Eur Heart J Suppl. 2020-11-18

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Acta Radiol. 2022-1

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Biomed Res Int. 2020

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Cardiac computed tomography radiomics: an emerging tool for the non-invasive assessment of coronary atherosclerosis.

Cardiovasc Diagn Ther. 2020-12

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Coronary Computed Tomography Angiography From Clinical Uses to Emerging Technologies: JACC State-of-the-Art Review.

J Am Coll Cardiol. 2020-9-8

[10]
Deep learning analysis in coronary computed tomographic angiography imaging for the assessment of patients with coronary artery stenosis.

Comput Methods Programs Biomed. 2020-11

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