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Elvas, L. B., Águas, P., Ferreira, J., Oliveira, J., Dias, J. & Rosário, L. B. (2023). AI-based aortic stenosis classification in MRI scans. Electronics. 12 (23)

L. M. Elvas et al.,  "AI-based aortic stenosis classification in MRI scans", in Electronics, vol. 12, no. 23, 2023

@article{elvas2023_1734574519474,
	author = "Elvas, L. B. and Águas, P. and Ferreira, J. and Oliveira, J. and Dias, J. and Rosário, L. B.",
	title = "AI-based aortic stenosis classification in MRI scans",
	journal = "Electronics",
	year = "2023",
	volume = "12",
	number = "23",
	doi = "10.3390/electronics12234835",
	url = "https://www.mdpi.com/2079-9292/12/23/4835"
}

TY  - JOUR
TI  - AI-based aortic stenosis classification in MRI scans
T2  - Electronics
VL  - 12
IS  - 23
AU  - Elvas, L. B.
AU  - Águas, P.
AU  - Ferreira, J.
AU  - Oliveira, J.
AU  - Dias, J.
AU  - Rosário, L. B.
PY  - 2023
SN  - 2079-9292
DO  - 10.3390/electronics12234835
UR  - https://www.mdpi.com/2079-9292/12/23/4835
AB  - Aortic stenosis (AS) is a critical cardiovascular condition that necessitates precise diagnosis for effective patient care. Despite a limited dataset comprising only 202 images, our study employs transfer learning to investigate the efficacy of five convolutional neural network (CNN) models, coupled with advanced computer vision techniques, in accurately classifying AS. The VGG16 model stands out among the tested models, achieving 95% recall and F1-score. To fortify the model’s robustness and generalization, we implement various data augmentation techniques, including translation, rotation, flip, and brightness adjustment. These techniques aim to capture real-world image variations encountered in clinical settings. Validation, conducted using authentic data from Hospital Santa Maria, not only affirms the clinical applicability of our model but also highlights the potential to develop robust models with a limited number of images. The models undergo training after the images undergo a series of computer vision and data augmentation techniques, as detailed in this paper. These techniques augment the size of our dataset, contributing to improved model performance. In conclusion, our study illuminates the potential of AI-driven AS detection in MRI scans. The integration of transfer learning, CNN models, and data augmentation yields high accuracy rates, even with a small dataset, as validated in real clinical cases.
ER  -