National Heart Centre of Singapore Pte Ltd.

A new deep learning model-based tool developed in South Korea can be used to both diagnose coronary artery disease and predict major adverse cardiac events in emergency cases. HOW IT WORKS Developed by a team of researchers from Yonsei University Severance Hospital, Keimyung University Dongsan Hospital, and medical imaging AI company Phantomics, the AI tool automatically assesses coronary CT angiography (CCTA) scans and classifies stenosis as normal, non-occlusive, or occlusive. The model also utilises the YOLO architecture, which simultaneously locates and classifies objects, to quickly process images. FINDINGS In a study, the AI model was tested using CCTA data from 408 patients who presented with acute chest pain at three emergency departments from 2018 to 2022. Findings, which were published in the Radiology: Artificial Intelligence journal of the Radiology Society of North America, noted that the deep learning-driven analysis of the degree of stenosis was a better predictor of major adverse cardiac events (MACEs) than common clinical risk factors such as hyperlipidemia or the cardiac enzyme level troponin-T. Moreover, pairing the AI-driven analysis with common risk factors improved the MACE prediction by 14% points to 90%. WHY IT MATTERS CT angiography, which is used to assess artery stenosis for CAD prognosis, usually takes a long time to process results, with analyses varying depending on the reader, according to Severance Hospital. The AI tool developed by the Korean research team not only detects CAD but also predicts MACE risks in patients who present to the emergency department. "This study suggests the possibility that deep learning models can be applied to predict patient prognosis beyond simply determining the presence or absence of CAD in emergency rooms, where rapid diagnosis and treatment decisions are important," said Dr Jin Hur, professor at Severance Hospital's Department of Radiology. "The AI technology can be applied beyond simple diagnostic assistance to become a clinical decision support tool," he added. MARKET SNAPSHOT Latest research projects across Asia-Pacific have also utilised AI to improve CAD diagnosis. Singaporean startup Health BETA is developing a solution that considers genetic and lifestyle factors in providing an enhanced polygenic risk score for CAD. Meanwhile, three major heart hospitals in Singapore – the National Heart Centre Singapore, the National University Hospital and Tan Tock Seng Hospital – are set to pilot a new machine learning-driven system for rapid CAD prediction. In Australia, publicly listed medical device companies Echo IQ and Artrya have recently obtained 510(k) clearance from the United States Food and Drug Administration for their respective AI-powered software for diagnosing CAD. Echo IQ's product is specifically indicated for detecting severe aortic stenosis, while Artrya's AI-powered software, Salix, delivers a 10-minute point-of-care assessment of CCTA scans.