About this Research Topic
Minimally invasive therapies are one of the rapidly emerging and promising alternative treatment modalities for curative and palliative treatment of vast varieties of benign and malignant tumors, cardiovascular diseases, neurodegenerative disorders, as well as mitigating various types of chronic pain. Typical advantages associated with these therapeutic modalities are lower treatment cost, reduction in morbidity and mortality rates, shorter recovery time, and better cosmesis. A significant role in the advancement of minimally invasive therapies is played by the better diagnostic and screening techniques enabling the diagnosis of the disease at the earlier stage. The application of different types of nanomaterials has also been explored among these therapies to attain a precise control of local/selective damage to the culprit tissue. More recently, the tremendous potential of biosensors has also been elucidated in providing real-time feedback of the critical parameters during the therapeutic procedures. Computational modeling and simulations have also emerged as a powerful tool for better understanding the underlying biophysical principles, and the effects of intrinsic and extrinsic factors on the efficacy of minimally invasive therapeutic procedures. Computational modeling provides a quick, convenient, and inexpensive a priori evaluation of the treatment outcomes and thus plays a vital role in the design and development of new protocols and their optimization in further enhancement of the treatment efficacy.
The goal of this Research Topic is to present novel modelling, computational, experimental, and clinical studies in the field of minimally invasive theranostics to advance this exciting and interdisciplinary field of research. In this context, it is critical to developing new advanced models, methodologies, and tools, including state-of-the-art computational models based on multiscale, multiphysics, and bio-networks approaches in all areas of theranostics, as well as to foster the studies based on recent achievements of data-driven techniques, artificial intelligence, and machine learning. Many new results in these areas are expected to be translated into the clinical work-flow and assist the physicians in the diagnostics and disease treatment. A non-exhaustive list of topics of interest for this Research Topic is given next.
Contributions are welcome on any aspects (modeling/experimental/clinical applications) pertaining to the overall scope/focus of this issue. Topics of particular interest include, but are not limited to:
∙ Minimally invasive devices for disease treatments, including cardiovascular, nervous, cancer, chronic pain, with technologies ranging from laser, radiofrequency, microwave, and high-intensity focused ultrasound ablations to cryoablation, and irreversible electroporation;
∙ Nano/biomaterials assisted theranostics translated into medical devices(including 3D/4D printing technologies, smart biomaterials, etc.);
∙ Medical devices and networks, including network-based models of infectious diseases and associated areas of medical devices;
∙ Use of biomarkers in medical devices for screening, diagnostics, and other applications;
∙ Neurodegenerative biosensing, medical devices, and technologies for neurodegenerative disease detection and treatments;
∙ Signal and image processing technologies utilized in medical devices for biomedical applications (MRI, EEG, ECG, etc.);
∙ Medical device designs assisted by multiscale modelling from nano to macroscales;
∙ Statistical learning (including Machine learning/Deep learning applications) and Artificial intelligence approaches in theranostics as they relate to medical devices;
∙ Complex system approaches in biomedical applications and technology, brain connectomics, and networks in neurodegenerative diseases as they relate to medical devices.
Keywords: Minimally Invasive Therapies; Biosensors and Smart Sensor Systems; Mathematical Modeling and Simulations in Theranostics; Multiscale, Bio-networks and Data-driven Models; Artificial Intelligence and Machine-learning Algorithms
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