Abstract：
OBJECTIVE: Coronary computed tomography angiography provides noninvasive assessment of coronary stenosis severity and flow impairment. Automated artificial intelligence analysis may assist in precise quantification and characterization of coronary atherosclerosis, enabling patient-specific risk determination and management strategies. This multicenter international study compared an automated deep-learning-based method for segmenting coronary atherosclerosis in coronary computed tomography angiography (CCTA) against the reference standard of intravascular ultrasound (IVUS).
RESULTS: The study included clinically stable patients with known coronary artery disease from 15 centers in the U.S. and Japan. An artificial intelligence (AI)-enabled plaque analysis service was utilized to quantify and characterize total plaque (TPV), vessel, lumen, calcified plaque (CP), non-calcified plaque (NCP), and low attenuation plaque (LAP) volumes derived from CCTA and compared with IVUS measurements in a blinded, core laboratory-adjudicated fashion. In 237 patients, 432 lesions were assessed; mean lesion length was 24.5 mm. Mean IVUS-TPV was 186.0 mm3. AI-enabled plaque analysis on CCTA showed strong correlation and high accuracy when compared with IVUS; correlation coefficient, slope, and Y intercept for TPV were 0.91, 0.99, and 1.87, respectively; for CP volume 0.91, 1.05, and 5.32, respectively; and for NCP volume 0.87, 0.98, and 15.24, respectively. Bland-Altman analysis demonstrated strong agreement with little bias for these measurements.
CONCLUSIONS: Artificial intelligence enabled CCTA quantification and characterization of atherosclerosis demonstrated strong agreement with IVUS reference standard measurements. This tool may prove effective for accurate evaluation of coronary atherosclerotic burden and cardiovascular risk assessment.[ClinicalTrails.gov identifier: NCT05138289].
RESULTS: The study included clinically stable patients with known coronary artery disease from 15 centers in the U.S. and Japan. An artificial intelligence (AI)-enabled plaque analysis service was utilized to quantify and characterize total plaque (TPV), vessel, lumen, calcified plaque (CP), non-calcified plaque (NCP), and low attenuation plaque (LAP) volumes derived from CCTA and compared with IVUS measurements in a blinded, core laboratory-adjudicated fashion. In 237 patients, 432 lesions were assessed; mean lesion length was 24.5 mm. Mean IVUS-TPV was 186.0 mm3. AI-enabled plaque analysis on CCTA showed strong correlation and high accuracy when compared with IVUS; correlation coefficient, slope, and Y intercept for TPV were 0.91, 0.99, and 1.87, respectively; for CP volume 0.91, 1.05, and 5.32, respectively; and for NCP volume 0.87, 0.98, and 15.24, respectively. Bland-Altman analysis demonstrated strong agreement with little bias for these measurements.
CONCLUSIONS: Artificial intelligence enabled CCTA quantification and characterization of atherosclerosis demonstrated strong agreement with IVUS reference standard measurements. This tool may prove effective for accurate evaluation of coronary atherosclerotic burden and cardiovascular risk assessment.[ClinicalTrails.gov identifier: NCT05138289].
摘要：
目的：冠状动脉CT血管造影提供冠状动脉狭窄严重程度和血流损害的无创性评估。自动人工智能分析可能有助于精确量化和表征冠状动脉粥样硬化，使患者特定的风险确定和管理策略。这项多中心国际研究比较了一种基于深度学习的自动化方法，用于在冠状动脉计算机断层扫描血管造影术（CCTA）中分割冠状动脉粥样硬化与血管内超声（IVUS）的参考标准。
结果：该研究纳入了来自美国和日本15个中心的已知冠状动脉疾病的临床稳定患者。采用人工智能（AI）支持的斑块分析服务来量化和表征总斑块（TPV），船只,管腔,钙化斑块(CP),非钙化斑块（NCP），和从CCTA获得的低衰减斑块(LAP)体积，并与盲管中的IVUS测量结果进行比较，核心实验室裁决的时尚。在237名患者中，评估了432个病变;平均病变长度为24.5mm。平均IVUS-TPV为186.0mm3。与IVUS相比，CCTA的AI启用斑块分析显示出强相关性和高准确性；相关系数，斜坡,TPV和Y截距分别为0.91、0.99和1.87；CP体积分别为0.91、1.05和5.32；NCP体积分别为0.87、0.98和15.24。Bland-Altman分析显示，这些测量结果具有很强的一致性，几乎没有偏差。
结论：人工智能使CCTA定量和动脉粥样硬化的表征与IVUS参考标准测量结果非常吻合。该工具可有效用于准确评估冠状动脉粥样硬化负担和心血管风险评估。[ClinicalTrails.gov标识符:NCT05138289]。
结果：该研究纳入了来自美国和日本15个中心的已知冠状动脉疾病的临床稳定患者。采用人工智能（AI）支持的斑块分析服务来量化和表征总斑块（TPV），船只,管腔,钙化斑块(CP),非钙化斑块（NCP），和从CCTA获得的低衰减斑块(LAP)体积，并与盲管中的IVUS测量结果进行比较，核心实验室裁决的时尚。在237名患者中，评估了432个病变;平均病变长度为24.5mm。平均IVUS-TPV为186.0mm3。与IVUS相比，CCTA的AI启用斑块分析显示出强相关性和高准确性；相关系数，斜坡,TPV和Y截距分别为0.91、0.99和1.87；CP体积分别为0.91、1.05和5.32；NCP体积分别为0.87、0.98和15.24。Bland-Altman分析显示，这些测量结果具有很强的一致性，几乎没有偏差。
结论：人工智能使CCTA定量和动脉粥样硬化的表征与IVUS参考标准测量结果非常吻合。该工具可有效用于准确评估冠状动脉粥样硬化负担和心血管风险评估。[ClinicalTrails.gov标识符:NCT05138289]。
