Comparison of artificial intelligence models for heart disease detection using magnetic resonance imaging
DOI:
https://doi.org/10.24054/rcta.v1i47.4285Keywords:
heart disease, MRI, YOLOv8, Random Forest, CNN, artificial intelligenceAbstract
In this study, three artificial intelligence techniques were compared for the binary detection (healthy/diseased) of cardiopathies using axial slices from cardiac magnetic resonance imaging. Using a dataset of 150 patients in NIfTI format, the images were preprocessed (normalization, rescaling to 128 × 128, RGB conversion, and data augmentation) and split on a per-patient basis using an 80/20 ratio. A Random Forest model with GLCM radiomic descriptors and first-order statistical features, a convolutional neural network (CNN), and a YOLOv8-based model adapted for binary classification were evaluated. The models were compared using accuracy, precision, recall, F1-score, and AUC, and explainability techniques (SHAP, Grad-CAM, Integrated Gradients, and occlusion sensitivity) were applied to validate the anatomical coherence of the predictions. Overall, the results indicate that pretrained deep learning approaches, such as YOLOv8, offer substantial advantages in terms of accuracy and interpretability, positioning them as a promising alternative for the development of intelligent decision-support systems for the diagnosis of structural cardiopathies.
Downloads
References
World Health Organization, “Cardiovascular diseases (CVDs),” 2023. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)
Ministerio de Salud y Protección Social, “Enfermedades cardiovasculares.” [Online]. Available: https://www.minsalud.gov.co/salud/Paginas/Enfermedades-cardiovasculares.aspx
Ministerio de Salud y Protección Social, “Minsalud conmemora el Día Mundial del Corazón,” Sep. 29, 2022. [Online]. Available: https://www.minsalud.gov.co/Paginas/Minsalud-conmemora-el-dia-mundial-del-Corazon.aspx
Ministerio de Salud y Protección Social, “Plan Nacional de Salud Pública 2022–2031.” [Online]. Available: https://www.minsalud.gov.co/plandecenal/Paginas/PDSP-2022-2031.aspx
D. De Santis, A. Trivisonno, and E. G. Caiani, “Accelerated deep learning-based function assessment in cardiovascular magnetic resonance: a clinical comparison with conventional cine sequences,” La Radiologia Medica, 2025, doi: https://doi.org/10.1007/s11547-025-02019-6
A. Argentiero, G. Muscogiuri, M. G. Rabbat, C. Martini, N. Soldato, P. Basile, et al., “The applications of artificial intelligence in cardiovascular magnetic resonance—A comprehensive review,” Journal of Clinical Medicine, vol. 11, no. 10, p. 2866, 2022, doi: https://doi.org/10.3390/jcm11102866
O. Bernard et al., “Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: Is the problem solved?” IEEE Trans. Med. Imaging, vol. 37, no. 11, pp. 2514–2525, 2018. https://doi.org/10.1109/TMI.2018.2837502.
M. Alsharqi and E. R. Edelman, “Artificial Intelligence in Cardiovascular Imaging and Interventional Cardiology: Emerging Trends and Clinical Implications,” J. Soc. Cardiovasc. Angiogr. Interv., vol. 4, no. 3, p. 102558, 2025. https://doi.org/10.1016/j.jscai.2024.102558
Y.-R. Wang, K. Yang, Y. Wen, P. Wang, Y. Hu, Y. Lai, Y. Wang, K. Zhao, S. Tang, A. Zhang, H. Zhan, M. Lu, X. Chen, S. Yang, Z. Dong, Y. Wang, H. Liu, L. Zhao, L. Huang, Y. Li, L. Wu, Z. Chen, Y. Luo, D. Liu, et al., “Screening and diagnosis of cardiovascular disease using artificial intelligence-enabled cardiac magnetic resonance imaging,” Nature Medicine, vol. 30, pp. 1471–1480, 2024, doi: https://doi.org/10.1038/s41591-024-02971-2
R. Jafari, R. Azad, and S. Minaee, “Automatic diagnosis of myocarditis disease in cardiac MRI modality using deep transformers and explainable artificial intelligence,” arXiv preprint arXiv:2210.14611, 2022. https://doi.org/10.48550/arXiv.2210.14611
D. Li, H. Zhang, and C. Wu, “An algorithm for cardiac disease detection based on magnetic resonance imaging using an improved SA-YOLO model,” Scientific Reports, vol. 15, 88567, 2025, doi: https://doi.org/10.1038/s41598-025-88567-3
M. U. Rehman, S. Akbar, and A. Imran, “Predicting coronary heart disease with advanced machine learning classifiers for improved cardiovascular risk assessment,” Scientific Reports, vol. 15, 96437, 2025, doi: https://doi.org/10.1038/s41598-025-96437-1
T. Tsampras, T. Karamanidou, G. Papanastasiou, and T. G. Stavropoulos, “Deep learning for cardiac imaging: Focus on myocardial diseases: A narrative review,” Hellenic Journal of Cardiology, 2024. https://doi.org/10.1016/j.hjc.2024.12.002
F. Cheng et al., “Learning directional feature maps for cardiac MRI segmentation,” in Proc. Int. Conf. Med. Image Comput. Comput.-Assist. Intervent. (MICCAI), Cham, Switzerland: Springer, Sep. 2020, pp. 108–117. https://doi.org/10.1007/978-3-030-59719-1_11
D. M. Alsekait et al., “Heart-Net: A multi-modal deep learning approach for diagnosing cardiovascular diseases,” Comput. Mater. Contin., vol. 80, no. 3, 2024. https://doi.org/10.32604/cmc.2024.054591
R. K. Sahu, D. R. Sahu, and B. K. Pattanayak, “A hybrid segmentation and classification CAD framework for myocardial infarction detection using cardiac MRI,” Scientific Reports, vol. 15, 98893, 2025, doi: https://doi.org/10.1038/s41598-025-98893-1
X. Liu, F. Xing, H. K. Gaggin, C. C. J. Kuo, G. El Fakhri, and J. Woo, “Successive subspace learning for cardiac disease classification with two-phase deformation fields from cine MRI,” in Proc. 2023 IEEE 20th Int. Symp. Biomed. Imaging (ISBI), Apr. 2023, pp. 1–5. https://doi.org/10.1109/ISBI53787.2023.10230746
J. Xing, S. Wang, K. C. Bilchick, A. R. Patel, and M. Zhang, “Joint deep learning for improved myocardial scar detection from cardiac MRI,” in Proc. 2023 IEEE 20th Int. Symp. Biomed. Imaging (ISBI), Apr. 2023, pp. 1–5. https://doi.org/10.1109/ISBI53787.2023.10230541
G. P. Veldhuizen, T. Lenz, D. Cifci, M. van Treeck, J. Clusmann, Y. Chen, C. V. Schneider, T. Luedde, P. W. de Leeuw, A. El-Armouche, D. Truhn, and J. N. Kather, “Deep learning can predict cardiovascular events from liver imaging,” JHEP Reports, 2025, Art. no. 101427. https://doi.org/10.1016/j.jhepr.2025.101427
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-CAM: Visual explanations from deep networks via gradient-based localization,” International Journal of Computer Vision, vol. 128, pp. 336–359, 2019, https://doi.org/10.1007/s11263-019-01228-7
J. Liu, A. Wei, L. Cao, X. He, and C. Tang, “Contrastive trustworthy prototype learning for multi-modality myocardial pathology segmentation,” Applied Soft Computing, vol. 173, Art. no. 112909, 2025, doi: https://doi.org/10.1016/j.asoc.2025.112909
Z. Dong, Y. Tang, P. Sun, G. Yin, K. Zhao, X. Ma, and S. Zhao, “Early identification of myocardial microstructural alterations in hypertrophic cardiomyopathy with in vivo cardiac diffusion-tensor imaging,” Radiology: Cardiothoracic Imaging, vol. 7, no. 1, Art. no. e240009, 2025, doi: https://doi.org/10.1148/ryct.240009
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Jorge Eliecer Gomez Gomez, Juan José Gúzman Pineda, Velssy Liliana Hernández Riaño
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
