Integrated Biomedical Signal and Image Processing Techniques for Enhanced Disease Diagnosis and Clinical Decision Support

Abstract

Human life has an invaluable worth, and its protection has always been a central priority for healthcare systems. Recent advances in computerized medical image reconstruction, together with developments in analysis techniques and computer-assisted diagnosis, have significantly enhanced medical imaging, enabling more accurate diagnoses and more targeted treatment strategies. In this context, Internet of Medical Things solutions, such as telerehabilitation, provide concrete answers to many healthcare needs, allowing remote patient support and ensuring continuity of care. The natural progression of this path calls for closer collaboration between clinicians and biomedical engineers, with the goal of developing innovative solutions and helping to transform the future of healthcare. Digital processing of biomedical signals and images, once confined to research laboratories, is now an essential resource in medical applications such as early disease detection, monitoring, and treatment planning. The aim of this work is to integrate signal and image processing techniques, together with machine learning algorithms, into diagnostic practice and telerehabilitation pathways. Both research directions are introduced simultaneously. Once the technical aspects have been outlined, their practical implementation is discussed through examples related to selected diseases.

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