A Study on Cross-Applicability and Potential of Machine Learning Tools in Hip and Dental Biomechanics

Fadi Aldakheel; Yousef Heider; Marco Haertlé; Peter Wriggers; Hans Jürgen Maier; Meike Stiesch

doi:10.1007/978-3-031-93213-7_1

A Study on Cross-Applicability and Potential of Machine Learning Tools in Hip and Dental Biomechanics

Fadi Aldakheel^*, Yousef Heider, Marco Haertlé, Peter Wriggers, Hans Jürgen Maier, Meike Stiesch

^*Korrespondierende*r Autor*in für diese Arbeit

Medizinische Hochschule Hannover (MHH)

Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Beitrag in Buch/Sammelwerk › Forschung › Peer-Review

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.

Originalsprache	Englisch
Titel des Sammelwerks	Advances and Challenges in Computational Mechanics
Herausgeber (Verlag)	Springer Nature
Seiten	1-15
Seitenumfang	15
ISBN (elektronisch)	9783031932137
ISBN (Print)	9783031932120
DOIs	https://doi.org/10.1007/978-3-031-93213-7_1
Publikationsstatus	Veröffentlicht - 2 Jan. 2026

ASJC Scopus Sachgebiete

Allgemeiner Maschinenbau
Allgemeine Mathematik

Zugriff auf Dokument

10.1007/978-3-031-93213-7_1

Andere Dateien und Links

Verknüpfung zur Publikation in Scopus

SIIRI: Sonderforschungsbereich-Transregio 298/2, Teilprojekt A07: Surrogat-Modelle zum Monitoring von Implantaten
Aldakheel, F. (Projektleiter*in (Principal Investigator)), Nogueira, W. (Projektleiter*in (Principal Investigator)) & Haertlé, M. (Projektleiter*in (Principal Investigator))
1 Jan. 2026 → 30 Juni 2029
Projekt: Forschung
SIIRI: Sonderforschungsbereich-Transregio 298/2, Teilprojekt B04: Aktive Stimulus-responsive Implantate
Klose, C. (Projektleiter*in (Principal Investigator)), Pott, P.-C. (Projektleiter*in (Principal Investigator)), Maier, H. J. (Projektleiter*in (Principal Investigator)) & Wurz, M. C. (Projektleiter*in (Principal Investigator))
1 Jan. 2026 → 30 Juni 2029
Projekt: Forschung

Dieses zitieren

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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.",

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A Study on Cross-Applicability and Potential of Machine Learning Tools in Hip and Dental Biomechanics. / Aldakheel, Fadi; Heider, Yousef; Haertlé, Marco et al.
Advances and Challenges in Computational Mechanics. Springer Nature, 2026. S. 1-15.

Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Beitrag in Buch/Sammelwerk › Forschung › Peer-Review

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AU - Maier, Hans Jürgen

AU - Stiesch, Meike

N1 - Publisher Copyright: © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

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AB - 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.

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ER -

A Study on Cross-Applicability and Potential of Machine Learning Tools in Hip and Dental Biomechanics

Abstract

ASJC Scopus Sachgebiete

Zugriff auf Dokument

Andere Dateien und Links

Projekte

SIIRI: Sonderforschungsbereich-Transregio 298/2, Teilprojekt A07: Surrogat-Modelle zum Monitoring von Implantaten

SIIRI: Sonderforschungsbereich-Transregio 298/2, Teilprojekt B04: Aktive Stimulus-responsive Implantate

Dieses zitieren