Artificial Intelligence and Advanced Computational Techniques for Precision Medicine

Dear Colleagues,

Nowadays, there is a growing interest in healthcare processes that quantitatively analyze biomedical data using computer-assisted techniques. Health informatics make possible to acquire further insights into diseases, thus enabling the development and the implementation of clinical decision support systems (CDSSs).

The great variability of this scenario is linked to both the different diseases that are experienced by patients and the specific clinical reader (inter- and intra- observer dependence) that analyzes and identifies the disease and that decides the most adequate treatment process, and both of these factors must be considered.

Researchers coupled with expert clinicians can play an important role in turning complex medical data into actionable knowledge that ultimately improves patient care.

In recent years, there has been a revolution in which the combined use of different types of data, both imaging (e.g., multi-modality imaging) and clinical data, allowing bio-markers that are capable of characterizing diseases to be identified. On the other hand, the real-time acquisition of physicians (e.g., motion recording analysis) allows researchers to produce a continuous flow of data that can be used to better train machine learning algorithms, offering more precise results.

Among the new tools that are available, such as  bioimages, radiomics has experienced a great deal of diffusion and, if used properly, represents a powerful tool to support treatment and to customize the care of specific patients.

CDSSs—through Artificial Intelligence and computational techniques—enable inter- and intra-observer dependence to be reduced and make it possible to enhance result repeatability. In particular, the use of CDSSs can help clinicians in all phases of the treatment process, from diagnosis to therapeutic treatment planning, as well as to support prognosis.

This Special Issue will provide a forum through which researchers can publish original research papers covering state-of-the-art and novel algorithms, techniques, and applications of Artificial Intelligence methods, ranging from those implementing classic machine-learning to deep-learning, for biomedical data analysis aiming at precision medicine.

Topics of interest include but are not limited to:

• Health informatics;
• Biomedical image processing;
• Machine-learning and deep-learning techniques for image analysis;
• Artificial intelligence for precision medicine;
• Clinical decision support systems (c-DSSs);
• Physiological signal analysis;
• Radiomic analyses and models for personalized medicine;
• Deep learning for neuroimaging and oncological imaging analysis;
• Computational intelligence for single-cell data analysis;
• Detection, quantification, and characterization of ROIs from multi-modal medical images;
• Co-registration and fusion of multi-modality imaging for treatment planning and image-guided intervention;
• Motion and gait analysis in neurodegenerative diseases;
• Data analytics for healthcare.

Dr. Vincenzo Conti
Dr. Carmelo Militello
Prof. Dr. Wellington Pinheiro Dos Santos
Dr. Ganesh Naik
Dr. Nicole Dalia Cilia
Guest Editors

Manuscript Submission Information

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