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

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

BMJ Open

10.1136/bmjopen-2024-089047

https://doi.org/10.1136/bmjopen-2024-089047

Lohendran Baskaran, Shuang Leng, Utkarsh Dutta, Lynette Teo, Min Sen Yew, Ching-Hui Sia, Nicholas WS Chew, Weimin Huang, Hwee Kuan Lee, Roger Vaughan, Kee Yuan Ngiam, Zhongkang Lu, Xiaohong Wang, Eddy Wei Ping Tan, Nicholas Zi Yi Cheng, Swee Yaw Tan, Mark Y Chan, Liang Zhong

Computed Tomography Angiography; Coronary Artery Disease; Artificial Intelligence

02 December 2024

Baskaran, L., Leng, S., Dutta, U., Teo, L., Yew, M. S., Sia, C.-H., Chew, N. W., Huang, W., Lee, H. K., Vaughan, R., Ngiam, K. Y., Lu, Z., Wang, X., Tan, E. W. P., Cheng, N. Z. Y., Tan, S. Y., Chan, M. Y., & Zhong, L. (2024). Cohort profile: AI-driven national Platform for CCTA for clinicaL and industriaL applicatiOns (APOLLO). BMJ Open, 14(12), e089047. https://doi.org/10.1136/bmjopen-2024-089047

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. Participants: 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. Findings to date: 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. Future plans: 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. Trial registration numberNCT05509010.

Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)

This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - Industry Alignment Fund – Pre-positioning Programme
Grant Reference no. : H20c6a0035

This research / project is supported by the National Medical Research Council (NMRC) of Singapore - Centre Grant: Program for Transforming and Evaluating Outcomes in Cardiometabolic disease (PROTECT)
Grant Reference no. : CG21APR1006

This research / project is supported by the National Medical Research Council (NMRC) of Singapore - Transitional Award Grant: Improving Obstructive Coronary Artery Disease and Cardiovascular Risk Prediction Using Deep Learning Analysis on Coronary Artery Calcium Imaging
Grant Reference no. : TA21nov-0001

© Author(s) (or their employer(s)) 2024. Re-­use permitted under CC BY­-NC. No commercial re-­use. Published by BMJ.

https://oar.a-star.edu.sg/communities-collections/articles/21459

2044-6055

Institute for Infocomm Research