Multi-Sensor Fusion of Biomedical Data: Application to Diagnosis and Treatment

Dear Colleagues,

In the last decade, the use of computational and artificial intelligence has played a growing role in the exploitation of biomedical data from multiple sensors, improving medical diagnostics and enabling smarter healthcare solutions. Extensive research has been dedicated to the effective use of multisensorial information, such as physical parameters of body activity (heart rate, neural activity, blood pressure, respiration), anatomical or functional information obtained by 2D/3D/4D imaging (CT, MRI, fMRI, PET, SPECT, ultrasound), and biochemical data (ions, metabolites, proteins), aiming to support the real-time or offline personalized decision-making process. The current Special Issue seeks to present and highlight emerging biomedical applications that use advanced sensor fusion strategies to reduce decision error probability and increase reliability. We encourage submissions that focus on any of the following aspects:

• Innovative data fusion strategies, such as Bayesian methods, graphical models, abductive reasoning, probabilistic data association and state estimation techniques, that promote predictive and preventive medical solutions;
• State-of-the-art frameworks employing signal or medical image analysis algorithms, as well as machine/deep learning techniques for extraction of biomarkers from multiple sensors that carry prognostic information on disease or therapy;
• Contributions that address the main challenges during data fusion attributed to the high dimensionality, heterogeneity, anatomical variability, noise, sparsity, and missing values in the data.

Sensor materials and technologies, hardware design for data integration, multimodal interfaces, embedded sensor fusion systems, mobile platform applications, and data management techniques for retrieval, storage, and transfer are not within the scope of this Special Issue.

Dr. Evangelia I. Zacharaki
Guest Editor

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