Advanced Detection of Coronary Artery Disease with Multimodality Imaging

K. Divya; G. Manasa; G. Dharani; T. Kumara Swamy; B. Viswanath

Advanced Detection of Coronary Artery Disease with Multimodality Imaging

Author(s)	Ms. K. Divya, G. Manasa, G. Dharani, T. Kumara Swamy, B. Viswanath
Country	India
Abstract	Coronary artery disease (CAD) is one of the leading causes of mortality in the world, and its effective treatment and management are possible only at early stages. The conventional systems of diagnosis frequently rely on single-modal imaging; it was not able to provide the sensitivity and specificity of the correct identification. The present project is a proposal of a developed CAD detection system that takes advantage of the type of multimodality images, including CT angiography, MRI and deep learning methods. Where each imaging modality can also contain high-level spatial features, as learned by the convolutional neural network (CNNs), temporal and sequential interactions among image frames (or among modalities) can be learned by recurrent neural networks (RNNs). The CNNs and the RNNs constitute a hybrid deep learning model that combines the spatial and time analysis advantages. CNNs achieve accuracy in the structural abnormality detection, whereas RNNs track changing patterns and interrelationships of the frame and modalities among frames. This birefringence leads to a more powerful and trustworthy system with less misclassification and facilitating care at low stages. Besides, the multimodality data has been utilized, which guarantees the usability of the system on various patient groups and imaging data, enhancing its use in clinical environments in practice. Finally, the suggested framework of the state-of-the-art CAD detection using various modalities and deep learning is an important step in the history of cardiovascular diagnosis. It extends the decision-support tool futuristic by closing the gap between structural and functional assessment tool to offer clinicians a cohesive decision-support tool that improves the diagnostic reliability and scientific accuracy. Timely and accurate diagnosis of the CAD leads not only to better treatment of patients but also contributes to effective costs of healthcare not only by reducing the use of unnecessary procedures but also preventing complications. Having the capability of combining multimodal image visualization and high levels of computational intelligence, it can result in the revolution of the clinical cardiology field and the introduction of data-driven intelligent diagnostic practice in the future.
Keywords	Coronary Artery disease, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), CT images.
Field	Engineering
Published In	Volume 7, Issue 4, April 2026
Published On	2026-04-04

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Advanced Detection of Coronary Artery Disease with Multimodality Imaging

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