Information Technology in Biomedicine

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Information Technology in Biomedicine is written by Pawel Badura and published by Springer Nature. It's available with International Standard Book Number or ISBN identification 303195582X (ISBN 10) and 9783031955822 (ISBN 13).

This book presents 10th International Conference Information Technologies in Biomedicine and shows research on Information Technologies in Medicine. The aim of this issue is to address the requirements for innovative technologies in computer-aided diagnosis and therapy and to indicate new areas of research, including the integration of multimodal data implemented in various fields of medical diagnostics. Fast and reliable data processing techniques able to deliver results in a user-friendly manner are discussed in three sections. First, the AI and Image Analysis Applications in Medicine section features AI-driven studies for image processing, including comparisons of deep learning models for nuclear detection and segmentation, and optimal scanning settings for biological samples. It also covers the importance of multimodal imaging analysis in radiological studies and chronic wound treatment. Then, Biomedical Signal Processing section investigates the effect of music on brain activity through EEG signal analysis. Artificial intelligence-assisted speech diagnostics in children focuses on sound segmentation and speech disorder detection. The effect of electronic cigarettes is tested by comparing the ECG of smokers and the control group. Finally, Modeling, Simulation, and Therapy Support section presents technologies in patient care, including telemedicine for rehabilitation, analysis of neurodegenerative diseases, and therapeutic applications of music. Models for supporting chronic wound treatment and amblyopia therapy are described. This book is a great reference tool for scientists who deal with problems of designing and implementing information processing tools employed in systems that assist the clinicians, radiologists, and physiotherapists in patient diagnosis and treatment. It also serves students in exploring innovations in quantitative medical data analysis, data mining, and artificial intelligence.