Fully Automated, Deep Learning, Cardiac CT-based Multimodal Network for Cardiovascular Risk Stratification in High-Risk Perioperative Patients
Date
Authors
Lu, Juan
Huangfu, Gavin
Ihdayhid, Abdul
Bennamoun, Mohammed
Konstantopoulos, John
Kwok, Simon
Niu, Kai
Liu, Yanbin
Figtree, Gemma A
Chan, Matthew TV
Supervisor
Item type
Journal Article
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Volume Title
Publisher
Oxford University Press
Abstract
AIMS: Major adverse cardiac events (MACE) significantly impact perioperative morbidity and mortality. We aimed to develop a fully automated multimodal deep learning (DL) system integrating patient demographics, comorbidities, and coronary computed tomography angiography (CCTA) findings to optimize risk prediction. METHODS AND RESULTS: We included 639 patients undergoing CCTA as part of perioperative risk assessment for elective non-cardiac surgery. Convolutional neural networks automatically identified coronary artery disease reporting and data system (CAD-RADS) scores and segmented the left ventricle, aorta, and heart. These imaging features were combined with patient demographics and comorbidities to predict MACE risk. We evaluated the performance of our multimodal model against the revised cardiac risk index (RCRI) using gradient boosting decision tree modelling and area under the receiver operating characteristic (AUROC) curves. Among 639 patients (mean age 70 ± 9 years, 56% males, median RCRI 1), 61% underwent orthopaedic surgery, 27% vascular surgery and the rest abdominal/pelvic or spine surgery. 45 patients experienced MACE within 30 days. Automated CAD-RADS (AUROC = 0.69) demonstrated comparable performance to human analysis (AUROC = 0.67, P = 0.77). The multimodal DL system (AUROC = 0.82) outperformed CAD-RADS (delta-AUROC = 0.13, CI: 0.02, 0.26, P = 0.02), and RCRI (delta-AUROC =0.22, CI: 0.05, 0.46; P = 0.001) in predicting MACE and demonstrated robust sensitivity (83%) and specificity (79%). CONCLUSION: Our multimodal system built using automated CAD-RADS, anatomical segmentations and patient demographics outperforms both human expert and automated CAD-RADS for MACE prediction. This approach has the potential to enhance patient outcomes by leveraging the synergy between automated imaging and clinical data.Description
Keywords
Convolutional neural networks, Coronary computed tomography angiography, Deep learning, Multimodal risk score, Perioperative risk, 32 Biomedical and Clinical Sciences, 3201 Cardiovascular Medicine and Haematology, Prevention, Biomedical Imaging, Machine Learning and Artificial Intelligence, Networking and Information Technology R&D (NITRD), Heart Disease, Cardiovascular, Patient Safety, Bioengineering, Cardiovascular, 3 Good Health and Well Being
Source
European Heart Journal: Digital Health, ISSN: 2634-3916 (Print); 2634-3916 (Online), Oxford University Press, 7(3), ztag037-. doi: 10.1093/ehjdh/ztag037
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Rights statement
© The Author(s) 2026. Published by Oxford University Press on behalf of the European Society of Cardiology. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.