Baskaran Lohendran, Leng Shuang, Dutta Utkarsh, Teo Lynette, Yew Min Sen, Sia Ching-Hui, Chew Nicholas Ws, Huang Weimin, Lee Hwee Kuan, Vaughan Roger, Ngiam Kee Yuan, Lu Zhongkang, Wang Xiaohong, Tan Eddy Wei Ping, Cheng Nicholas Zi Yi, Tan Swee Yaw, Chan Mark Y, Zhong Liang
Department of Cardiology, National Heart Centre Singapore, Singapore.
Duke-NUS Medical School, Singapore.
BMJ Open. 2024 Dec 2;14(12):e089047. doi: 10.1136/bmjopen-2024-089047.
Coronary CT angiography (CCTA) is well established for the diagnostic evaluation and prognostication of coronary artery disease (CAD). The growing burden of CAD in Asia and the emergence of novel CT-based risk markers highlight the need for an automated platform that integrates patient data with CCTA findings to provide tailored, accurate cardiovascular risk assessments. This study aims to develop an artificial intelligence (AI)-driven platform for CAD assessment using CCTA in Singapore's multiethnic population. We will conduct a hybrid retrospective-prospective recruitment of patients who have undergone CCTA as part of the diagnostic workup for CAD, along with prospective follow-up for clinical endpoints. CCTA images will be analysed locally and by a core lab for coronary stenosis grading, Agatston scoring, epicardial adipose tissue evaluation and plaque analysis. The images and analyses will also be uploaded to an AI platform for deidentification, integration and automated reporting, generating precision AI toolkits for each parameter.
CCTA images and baseline characteristics have been collected and verified for 4196 recruited patients, comprising 75% Chinese, 6% Malay, 10% Indian and 9% from other ethnic groups. Among the participants, 41% are female, with a mean age of 55±11 years. Additionally, 41% have hypertension, 51% have dyslipidaemia, 15% have diabetes and 22% have a history of smoking.
The cohort data have been used to develop four AI modules for training, testing and validation. During the development process, data preprocessing standardised the format, resolution and other relevant attributes of the images.
We will conduct prospective follow-up on the cohort to track clinical endpoints, including cardiovascular events, hospitalisations and mortality. Additionally, we will monitor the long-term impact of the AI-driven platform on patient outcomes and healthcare delivery.
NCT05509010.
冠状动脉CT血管造影（CCTA）在冠状动脉疾病（CAD）的诊断评估和预后判断方面已得到广泛应用。CAD在亚洲的负担日益加重，以及基于CT的新型风险标志物的出现，凸显了需要一个自动化平台，将患者数据与CCTA结果整合起来，以提供量身定制的、准确的心血管风险评估。本研究旨在开发一个由人工智能（AI）驱动的平台，用于在新加坡多民族人群中使用CCTA进行CAD评估。我们将对作为CAD诊断检查一部分而接受CCTA的患者进行回顾性与前瞻性相结合的招募，并对临床终点进行前瞻性随访。CCTA图像将在本地以及由一个核心实验室进行分析，以进行冠状动脉狭窄分级、阿加斯顿评分、心外膜脂肪组织评估和斑块分析。这些图像和分析结果还将上传到一个AI平台进行去识别、整合和自动报告，为每个参数生成精准的AI工具包。
已收集并核实了4196名招募患者的CCTA图像和基线特征，其中75%为华人，6%为马来人，10%为印度人，9%来自其他种族。参与者中，41%为女性，平均年龄为55±11岁。此外，41%患有高血压，51%患有血脂异常，15%患有糖尿病，22%有吸烟史。
队列数据已用于开发四个AI模块进行训练、测试和验证。在开发过程中，数据预处理对图像的格式、分辨率和其他相关属性进行了标准化。
我们将对该队列进行前瞻性随访，以追踪临床终点，包括心血管事件、住院和死亡率。此外，我们将监测AI驱动平台对患者结局和医疗服务的长期影响。
NCT05509010。
