Department of Internal Medicine, Thomas Jefferson University Medical Center, Philadelphia, PA, USA.
Department of Radiology and Division of Cardiology, The George Washington University School of Medicine, Washington, DC, USA.
Clin Imaging. 2022 Apr;84:149-158. doi: 10.1016/j.clinimag.2022.01.016. Epub 2022 Feb 3.
To determine whether coronary computed tomography angiography (CCTA) scanning, scan preparation, contrast, and patient based parameters influence the diagnostic performance of an artificial intelligence (AI) based analysis software for identifying coronary lesions with ≥50% stenosis.
CCTA is a noninvasive imaging modality that provides diagnostic and prognostic benefit to patients with coronary artery disease (CAD). The use of AI enabled quantitative CCTA (AI-QCT) analysis software enhances our diagnostic and prognostic ability, however, it is currently unclear whether software performance is influenced by CCTA scanning parameters.
CCTA and quantitative coronary CT (QCT) data from 303 stable patients (64 ± 10 years, 71% male) from the derivation arm of the CREDENCE Trial were retrospectively analyzed using an FDA-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. The algorithm's diagnostic performance measures (sensitivity, specificity, and accuracy) for detecting coronary lesions of ≥50% stenosis were determined based on concordance with QCA measurements and subsequently compared across scanning parameters (including scanner vendor, model, single vs dual source, tube voltage, dose length product, gating technique, timing method), scan preparation technique (use of beta blocker, use and dose of nitroglycerin), contrast administration parameters (contrast type, infusion rate, iodine concentration, contrast volume) and patient parameters (heart rate and BMI).
Within the patient cohort, 13% demonstrated ≥50% stenosis in 3 vessel territories, 21% in 2 vessel territories, 35% in 1 vessel territory while 32% had <50% stenosis in all vessel territories evaluated by QCA. Average AI analysis time was 10.3 ± 2.7 min. On a per vessel basis, there were significant differences only in sensitivity for ≥50% stenosis based on contrast type (iso-osmolar 70.0% vs non isoosmolar 92.1% p = 0.0345) and iodine concentration (<350 mg/ml 70.0%, 350-369 mg/ml 90.0%, 370-400 mg/ml 90.0%, >400 mg/ml 95.2%; p = 0.0287) in the context of low injection flow rates. On a per patient basis there were no significant differences in AI diagnostic performance measures across all measured scanner, scan technique, patient preparation, contrast, and individual patient parameters.
The diagnostic performance of AI-QCT analysis software for detecting moderate to high grade stenosis are unaffected by commonly used CCTA scanning parameters and across a range of common scanning, scanner, contrast and patient variables.
An AI-enabled quantitative CCTA (AI-QCT) analysis software has been validated as an effective tool for the identification, quantification and characterization of coronary plaque and stenosis through comparison to blinded expert readers and quantitative coronary angiography. However, it is unclear whether CCTA screening parameters related to scanner parameters, scan technique, contrast volume and rate, radiation dose, or a patient's BMI or heart rate at time of scan affect the software's diagnostic measures for detection of moderate to high grade stenosis. AI performance measures were unaffected across a broad range of commonly encountered scanner, patient preparation, scan technique, intravenous contrast and patient parameters.
确定冠状动脉计算机断层血管造影术（CCTA）扫描、扫描准备、对比剂和患者相关参数是否会影响基于人工智能（AI）的分析软件识别≥50%狭窄的冠状动脉病变的诊断性能。
CCTA 是一种非侵入性成像方式，可为冠状动脉疾病（CAD）患者提供诊断和预后益处。使用基于人工智能的定量 CCTA（AI-QCT）分析软件增强了我们的诊断和预后能力，但是，目前尚不清楚软件性能是否受到 CCTA 扫描参数的影响。
回顾性分析来自 CREDENCE 试验推导臂的 303 例稳定患者（64±10 岁，71%为男性）的 CCTA 和定量冠状动脉 CT（QCT）数据，使用经过 FDA 批准的基于云的软件，该软件可执行 AI 辅助的冠状动脉分割、管腔和血管壁确定、斑块定量和特征描述以及狭窄程度确定。该算法的诊断性能测量指标（敏感性、特异性和准确性）是基于与 QCA 测量结果的一致性确定的，随后根据扫描参数（包括扫描仪供应商、型号、单源与双源、管电压、剂量长度乘积、门控技术、计时方法）、扫描准备技术（β受体阻滞剂的使用、硝酸甘油的使用和剂量）、对比剂给药参数（对比剂类型、输注率、碘浓度、对比剂体积）和患者参数（心率和 BMI）进行比较。
在患者队列中，13%的患者在 3 个血管区域存在≥50%的狭窄，21%的患者在 2 个血管区域存在狭窄，35%的患者在 1 个血管区域存在狭窄，而 32%的患者在所有接受 QCA 评估的血管区域中狭窄程度<50%。平均 AI 分析时间为 10.3±2.7 分钟。基于血管的基础上，仅在≥50%狭窄的敏感性方面存在显著差异，这取决于对比剂类型（等渗对比剂为 70.0%，非等渗对比剂为 92.1%，p=0.0345）和碘浓度（<350mg/ml 为 70.0%，350-369mg/ml 为 90.0%，370-400mg/ml 为 90.0%，>400mg/ml 为 95.2%，p=0.0287），这与低注射流速有关。基于患者的基础上，在所有测量的扫描仪、扫描技术、患者准备、对比剂和个体患者参数中，AI 诊断性能测量指标没有显著差异。
用于检测中重度狭窄的 AI-QCT 分析软件的诊断性能不受常用 CCTA 扫描参数的影响，并且适用于广泛的常见扫描、扫描仪、对比剂和患者变量。
