Artificial intelligence applied to non-contrast-enhanced cardiac computed tomography for the prediction of cardiovascular events
| Autor: | Andrea Ripoli, V Zanetti, D. Della Latta, S Chiappino, Carla Luisa Susini, Valeria Piagneri, E Battipaglia, N Martini, Alberto Clemente, Michele Emdin, Alberto Aimo, Dante Chiappino |
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| Rok vydání: | 2020 |
| Předmět: |
Cardiovascular event
medicine.medical_specialty Cardiac computed tomography business.industry Deep learning medicine.disease Convolutional neural network Coronary Calcium Score Interobserver Variation Internal medicine Cardiology Medicine Non contrast enhanced Artificial intelligence Myocardial infarction Cardiology and Cardiovascular Medicine business |
| Zdroj: | European Heart Journal. 41 |
| ISSN: | 1522-9645 0195-668X |
| Popis: | Background Non-contrast-enhanced cardiac computed tomography (CT) may provide two measures that are emerging as independent predictors of cardiovascular events: coronary calcium score (CCS) and the volume of epicardial fat, a metabolically and immunologically active tissue surrounding the coronary arteries. The quantification of epicardial fat volume (EFV) is not routinely performed in clinical practice for the long time required for image reconstruction and the intra- and inter-observer variability. Purpose We evaluated if artificial intelligence (AI) might prove a valuable tool to interpret the CT data-set, and to better understand the relative prognostic value of CCS and EFV compared to “traditional” cardiovascular risk factors. Methods The Montignoso HEart and Lung Project is a community-based study carried out in a small town of Northern Tuscany (Italy). Starting from 2009, asymptomatic individuals from the general population underwent a baseline screening including a non-contrast cardiac CT, and were followed-up. For the present study, CCS and EFV were automatically measured from CT scans through a deep learning (DL) strategy based on convolutional neural networks. Because of the low incidence of the primary endpoint (myocardial infarction [MI]), the observed cardiac events were predicted with a random forest model built using a subsampling approach. Results Study participants (n=1528; 48% males, age 40 to 77 years) experienced 47 MI events (3%) over 5.5±1.5 years. CCS and EFV independently predicted this endpoint (p values Conclusions The tools of AI allow to perform an automated analysis of non-contrast-enhanced CT scans, with rapid and accurate measurement of CCS and EFV through a DL approach. In asymptomatic individuals from the general population, these features are more predictive of non-fatal MI than other variables related to the cardiovascular risk, as we can be demonstrated through an application of AI. Figure 1 Funding Acknowledgement Type of funding source: None |
| Databáze: | OpenAIRE |
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