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

^*Corresponding author for this work

Hannover Medical School (MHH)

Research output: Chapter in book/report/conference proceeding › Contribution to book/anthology › Research › 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.

Original language	English
Title of host publication	Advances and Challenges in Computational Mechanics
Publisher	Springer Nature
Pages	1-15
Number of pages	15
ISBN (Electronic)	9783031932137
ISBN (Print)	9783031932120
DOIs	https://doi.org/10.1007/978-3-031-93213-7_1
Publication status	Published - 2 Jan 2026

ASJC Scopus subject areas

General Engineering
General Mathematics

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

Cite this

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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.",

author = "Fadi Aldakheel and Yousef Heider and Marco Haertl{\'e} and Peter Wriggers and Maier, {Hans J{\"u}rgen} and Meike Stiesch",

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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. p. 1-15.

Research output: Chapter in book/report/conference proceeding › Contribution to book/anthology › Research › peer review

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T1 - A Study on Cross-Applicability and Potential of Machine Learning Tools in Hip and Dental Biomechanics

AU - Aldakheel, Fadi

AU - Heider, Yousef

AU - Haertlé, Marco

AU - Wriggers, Peter

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.

PY - 2026/1/2

Y1 - 2026/1/2

N2 - 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.

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

Abstract

ASJC Scopus subject areas

Access to Document

Other files and links

SIIRI: Collaborative Research Centre-Transregio 298/2, sub-project A07: Surrogate-modelling for monitoring of implants

SIIRI: Collaborative Research Centre-Transregio 298/2, sub-project B04: Active stimulus-responsive implants

Cite this

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

Abstract

ASJC Scopus subject areas

Access to Document

Other files and links

Projects

SIIRI: Collaborative Research Centre-Transregio 298/2, sub-project A07: Surrogate-modelling for monitoring of implants

SIIRI: Collaborative Research Centre-Transregio 298/2, sub-project B04: Active stimulus-responsive implants

Cite this