Cohort profile: AI-driven national Platform for CCTA for clinicaL and industriaL applicatiOns (APOLLO).

Journal: BMJ open
PMID: 39622571

Abstract

PURPOSE: 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.

Authors

  • Lohendran Baskaran
  • Shuang Leng
  • Utkarsh Dutta
    GKT School of Medical Education, King's College London, London, UK.
  • Lynette Teo
    Department of Diagnostic Imaging, National University Hospital, Singapore.
  • Min Sen Yew
    Department of Cardiology, Tan Tock Seng Hospital, Singapore.
  • Ching-Hui Sia
    Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
  • Nicholas Ws Chew
    Department of Cardiology, National University Hospital, Singapore, Singapore.
  • Weimin Huang
  • Hwee Kuan Lee
    Daniel Aitor Holdbrook, Malay Singh, Emarene Mationg Kalaw, and Hwee Kuan Lee, Bioinformatics Institute; Malay Singh and Hwee Kuan Lee, National University of Singapore; Yukti Choudhury and Min-Han Tan, Lucence Diagnostics; Yukti Choudhury and Min-Han Tan, Institute of Bioengineering and Nanotechnology; Valerie Koh, Puay Hoon Tan, and John Yuen Shyi Peng, Singapore General Hospital; Hui Shan Tan, Ravindran Kanesvaran, and Min-Han Tan, National Cancer Center Singapore; and Hwee Kuan Lee, Institute for Infocomm Research, Singapore.
  • Roger Vaughan
    Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore; Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore.
  • Kee Yuan Ngiam
    Group Chief Technology Office, National University Health System Singapore, Singapore, Singapore.
  • Zhongkang Lu
    Institute for Infocomm Research, Agency for Science Technology and Research, Singapore.
  • Xiaohong Wang
    School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China. wxhong@buaa.edu.cn.
  • Eddy Wei Ping Tan
    Bioinformatics Institute, Agency for Science Technology and Research, Singapore.
  • Nicholas Zi Yi Cheng
    Bioinformatics Institute, Agency for Science Technology and Research, Singapore.
  • Swee Yaw Tan
    Department of Cardiology, National Heart Centre Singapore, Singapore.
  • Mark Y Chan
    National University Heart Centre, National University Hospital Singapore, Singapore 119074, Singapore.
  • Liang Zhong

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