A Study on Cross-Applicability and Potential of Machine Learning Tools in Hip and Dental Biomechanics
Abstract
Machine learning (ML) is transforming hip joint and dental biomechanics by analyzing complex data, identifying patterns, and improving diagnostics, treatment, and rehabilitation. Despite anatomical differences, both fields share fundamental biomechanical principles, particularly in hard tissue interactions under mechanical load. This study reviews research and explores the cross-applicability of ML tools in hip and dental biomechanics, including gait analysis, predictive modeling, multiscale modeling, and wear analysis. Leveraging shared principles through transfer learning, ML fosters cost-effective solutions and reduces the need for extensive data collection.
Details
- Organisationseinheit(en)
-
Institut für Baumechanik und Numerische Mechanik
Institut für Kontinuumsmechanik
Institut für Werkstoffkunde
- Externe Organisation(en)
-
Medizinische Hochschule Hannover (MHH)
- Typ
- Beitrag in Buch/Sammelwerk
- Seiten
- 1-15
- Anzahl der Seiten
- 15
- Publikationsdatum
- 02.01.2026
- Publikationsstatus
- Veröffentlicht
- Peer-reviewed
- Ja
- ASJC Scopus Sachgebiete
- Allgemeiner Maschinenbau, Allgemeine Mathematik
- Elektronische Version(en)
-
https://doi.org/10.1007/978-3-031-93213-7_1 (Zugang:
Geschlossen
)
Zitieren
Laden...
