e-Algerian Journal of Health & Research
Volume 1, Numéro 3, Pages 01-21
2025-09-19
Application Of Ai Algorithms In Medical Imaging For The Detection And Classification Of Cardiovascular Diseases.
Authors : Bennezzar Zineb . Vladimirovna Khamenka Tatiana .
Abstract
Cardiovascular diseases (CVD) are the leading cause of global mortality, with 17.9 million annual deaths. In the face of this health challenge, traditional diagnostic methods (ECG, ultrasound) have limitations in terms of accessibility and effectiveness, requiring highly qualified personnel. The emergence of artificial intelligence (AI) opens promising prospects for automating and optimizing the diagnosis of CVD. This study explores the application of AI algorithms, notably convolutional neural networks (CNNs), transformers, and clustering methods, in the detection and classification of cardiovascular pathologies. The main objective is to evaluate the performance of these algorithms in terms of diagnostic accuracy, interpretability, and integration into existing healthcare systems. A review of the scientific literature highlights the potential of AI to handle large volumes of medical data, reduce human biases, and accelerate clinical decision-making. Preliminary results demonstrate that CNNs outperform conventional approaches in the analysis of medical images, while clustering techniques facilitate the visualization of complex data. However, challenges persist: large-scale adoption requires models compatible with hospital infrastructures, while ensuring data transparency and security. This research proposes an innovative methodology combining deep learning and comparative analysis to identify the most suitable algorithms for real clinical contexts. In conclusion, AI positions itself as an essential complementary tool for improving the quality of cardiovascular diagnostics, provided that the technical and ethical obstacles related to its deployment are overcome.
Keywords
Artificial intelligence ; cardiovascular diseases ; medical diagnosis ; neural networks ; clinical integration
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