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

Authors :
Ching-Hui Sia
Kee Yuan Ngiam
Xiaohong Wang
Roger Vaughan
Liang Zhong
Utkarsh Dutta
Weimin Huang
Lohendran Baskaran
Shuang Leng
Lynette Teo
Min Sen Yew
Nicholas WS Chew
Hwee Kuan Lee
Zhongkang Lu
Eddy Wei Ping Tan
Nicholas Zi Yi Cheng
Swee Yaw Tan
Mark Y Chan
Source :
BMJ Open, Vol 14, Iss 12 (2024)
Publication Year :
2024
Publisher :
BMJ Publishing Group, 2024.

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.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 number NCT05509010.

Subjects

Subjects :
Medicine

Details

Language :
English
ISSN :
20446055
Volume :
14
Issue :
12
Database :
Directory of Open Access Journals
Journal :
BMJ Open
Publication Type :
Academic Journal
Accession number :
edsdoj.0a5e9e6332c742fd87e5590dafb67e05
Document Type :
article
Full Text :
https://doi.org/10.1136/bmjopen-2024-089047