Department of Radiology, Ohio State University College of Medicine, Columbus, OH, 43210, USA.
Department of Radiology, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL, 32224, USA.
J Digit Imaging. 2021 Jun;34(3):554-571. doi: 10.1007/s10278-021-00441-6. Epub 2021 Mar 31.
Coronary computed tomography angiography (CCTA) evaluation of chest pain patients in an emergency department (ED) is considered appropriate. While a "negative" CCTA interpretation supports direct patient discharge from an ED, labor-intensive analyses are required, with accuracy in jeopardy from distractions. We describe the development of an artificial intelligence (AI) algorithm and workflow for assisting qualified interpreting physicians in CCTA screening for total absence of coronary atherosclerosis. The two-phase approach consisted of (1) phase 1-development and preliminary testing of an algorithm for vessel-centerline extraction classification in a balanced study population (n = 500 with 50% disease prevalence) derived by retrospective random case selection, and (2) phase 2-simulated clinical Trialing of developed algorithm on a per-case (entire coronary artery tree) basis in a more "real-world" study population (n = 100 with 28% disease prevalence) from an ED chest pain series. This allowed pre-deployment evaluation of the AI-based CCTA screening application which provides vessel-by-vessel graphic display of algorithm inference results integrated into a clinically capable viewer. Algorithm performance evaluation used area under the receiver operating characteristic curve (AUC-ROC); confusion matrices reflected ground truth vs AI determinations. The vessel-based algorithm demonstrated strong performance with AUC-ROC = 0.96. In both phase 1 and phase 2, independent of disease prevalence differences, negative predictive values at the case level were very high at 95%. The rate of completion of the algorithm workflow process (96% with inference results in 55-80 s) in phase 2 depended on adequate image quality. There is potential for this AI application to assist in CCTA interpretation to help extricate atherosclerosis from chest pain presentations.
在急诊科（ED）对胸痛患者进行冠状动脉计算机断层扫描血管造影（CCTA）评估被认为是合适的。虽然“阴性”CCTA 解读支持直接将患者从 ED 出院，但需要进行劳动密集型分析，注意力分散会影响准确性。我们描述了一种人工智能（AI）算法和工作流程的开发，用于协助合格的 CCTA 筛查医生，以确定是否存在冠状动脉粥样硬化的完全缺失。该两阶段方法包括：（1）第一阶段——在通过回顾性随机病例选择得出的均衡研究人群（n=500，患病率为 50%）中，开发一种用于血管中心线提取分类的算法，并进行初步测试；（2）第二阶段——在来自 ED 胸痛系列的更“真实世界”的研究人群（n=100，患病率为 28%）中，按病例（整个冠状动脉树）的基础对开发的算法进行模拟临床试用。这允许在部署前评估基于 AI 的 CCTA 筛查应用程序，该应用程序以血管为单位显示算法推断结果的图形显示，并集成到具有临床能力的查看器中。算法性能评估使用接收器操作特征曲线下的面积（AUC-ROC）；混淆矩阵反映了真实值与 AI 判定值之间的差异。基于血管的算法表现出很强的性能，AUC-ROC=0.96。在第一阶段和第二阶段中，独立于疾病患病率的差异，病例级别的阴性预测值都非常高，达到 95%。第二阶段中算法工作流程的完成率（有推断结果的为 96%，用时 55-80 秒）取决于图像质量是否足够好。该 AI 应用程序有可能协助 CCTA 解读，以帮助从胸痛表现中识别出动脉粥样硬化。
