Artificial intelligence-derived coronary artery calcium scoring saves time and achieves close to radiologist-level accuracy accuracy on routine ECG-gated CT.

Autor: Chamberlin JH; Division of Cardiothoracic Radiology, Department of Radiology and Radiological Science, Medical University of South Carolina, Clinical Science Building, 96 Jonathan Lucas Street, Suite 210, MSC 323, Charleston, SC, 29425, USA., Abrol S; Division of Cardiothoracic Radiology, Department of Radiology and Radiological Science, Medical University of South Carolina, Clinical Science Building, 96 Jonathan Lucas Street, Suite 210, MSC 323, Charleston, SC, 29425, USA., Munford J; Division of Cardiothoracic Radiology, Department of Radiology and Radiological Science, Medical University of South Carolina, Clinical Science Building, 96 Jonathan Lucas Street, Suite 210, MSC 323, Charleston, SC, 29425, USA., O'Doherty J; Division of Cardiothoracic Radiology, Department of Radiology and Radiological Science, Medical University of South Carolina, Clinical Science Building, 96 Jonathan Lucas Street, Suite 210, MSC 323, Charleston, SC, 29425, USA.; Siemens Medical Solutions, Malvern, PA, USA., Baruah D; Division of Cardiothoracic Radiology, Department of Radiology and Radiological Science, Medical University of South Carolina, Clinical Science Building, 96 Jonathan Lucas Street, Suite 210, MSC 323, Charleston, SC, 29425, USA., Schoepf UJ; Division of Cardiothoracic Radiology, Department of Radiology and Radiological Science, Medical University of South Carolina, Clinical Science Building, 96 Jonathan Lucas Street, Suite 210, MSC 323, Charleston, SC, 29425, USA., Burt JR; Division of Cardiothoracic Radiology, Department of Radiology and Radiological Science, Medical University of South Carolina, Clinical Science Building, 96 Jonathan Lucas Street, Suite 210, MSC 323, Charleston, SC, 29425, USA.; Division of Cardiothoracic Radiology, Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, USA., Kabakus IM; Division of Cardiothoracic Radiology, Department of Radiology and Radiological Science, Medical University of South Carolina, Clinical Science Building, 96 Jonathan Lucas Street, Suite 210, MSC 323, Charleston, SC, 29425, USA. kabakus@musc.edu.
Jazyk: angličtina
Zdroj: The international journal of cardiovascular imaging [Int J Cardiovasc Imaging] 2024 Dec 16. Date of Electronic Publication: 2024 Dec 16.
DOI: 10.1007/s10554-024-03306-5
Abstrakt: Artificial Intelligence (AI) has been proposed to improve workflow for coronary artery calcium scoring (CACS), but simultaneous demonstration of improved efficiency, accuracy, and clinical stability have not been demonstrated. 148 sequential patients who underwent routine calcium-scoring computed tomography were retrospectively evaluated using a previously validated AI model (syngo. CT CaScoring VB60, Siemens Healthineers, Forscheim, Germany). CACS was performed by manual (Expert alone), semi-automatic (AI + expert review), and automatic (AI alone) methods. Time to complete and intraclass correlation coefficients were the primary endpoints. Secondary endpoints included differences in multiethnic study of atherosclerosis (MESA) percentiles and stratification by calcium severity. AI and expert CACS agreement was excellent (ICC = 0.951; 95% CI 0.933-0.964). The global median time was 15 ± 2 s for AI ("Automatic"), 38 ± 13 s for the AI + manual review ("Semiautomatic") and 45 ± 24 s for the manual segmentation. Automatic segmentation was faster than manual segmentation for all CACS severities (P < 0.001). AI computational time was independent of calcium burden. Global mean bias in Agatston score across all patients was 7.4 ± 102.6. The mean bias for global MESA score percentile was 2.1% ± 12%. 95% of error corresponded to a ± 10% difference in MESA score. The use of AI for CACS performs excellent accuracy, saves approximately 60% of time in comparison to manual review, and demonstrates low bias for clinical risk profiles. Time benefits are magnified for patients with high CACS. However, a semi-automatic approach is still recommended to minimize potential errors while maintaining efficiency.
Competing Interests: Declarations. Competing interests: The authors declare no competing interests.
(© 2024. The Author(s).)
Databáze: MEDLINE
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