Digital Twins for Clinical Diagnosis, Monitoring, and Therapy of Disease
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".
Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 4369
Special Issue Editor
Interests: soft tissue biomechanics; machine learning in biomechanics; computer-aided surgery
Special Issue Information
Dear Colleagues,
Patient-specific biomechanical models are the basis of development of digital twins that are driving progress in clinical diagnosis, monitoring, and therapy of diseases. This poses new challenges in the advancement of the development of digital twins based on these models. Therefore, this Special Issue is intended for the presentation of new ideas and experimental results in the field of digital twins based on patient-specific biomechanical models.
Areas relevant to Digital Twins based on biomechanical models for clinical diagnosis, monitoring, and therapy of disease include but are not limited to finite element computation, medical image analysis, soft tissue biomechanics, mechanobiology, computational biology, machine learning in biomechanics, and the processing of datasets from scientific experiments, sensor networks, medical instruments, and other sources. Hard tissue biomechanics, musculoskeletal biomechanics, and biomechanics of movement and posture and their application to digital twins are also topics of interest for this Special Issue.
This Special Issue will publish high-quality, original research papers, in the overlapping fields of:
- Soft tissue biomechanics;
- Computer-aided diagnosis, planning, and surgery;
- Computational biology;
- Machine learning in biomechanics;
- Mechanobiology;
- Cardiovascular biomechanics;
- Ocular biomechanics;
- Spine biomechanics;
- Hard tissue biomechanics;
- Musculoskeletal biomechanics;
- Biomechanics of movement and posture.
Dr. Maria Jose Ruperez
Guest Editor
Manuscript Submission Information
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Keywords
- soft and hard tissue biomechanics
- finite element methods
- sensor-based evaluation of movement
- machine learning in biomechanics
- in vitro/in silico mechanobiology
- modeling and simulation of movement
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