Optimization of primary screw stability in Trabecular bone using neural network-based models

Multiple linear regression models were found to be insufficient for generating optimized screw configurations, and more complex surrogate models, such as NN, are needed. It could be concluded that different trabecular bone morphologies can benefit from distinct optimal screw designs. The insights gained from this study could have implications for the development of patient-specific orthopaedic treatments.

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Publication Details

Journal
Computer Methods and Programs in Biomedicine
Published
2025-03-11
DOI
https://doi.org/10.1016/j.cmpb.2025.108720
Primary Topic
Medical Imaging and Analysis
Type
article
Field-Weighted Citation Impact
0.00

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article

Optimization of primary screw stability in Trabecular bone using neural network-based models

Benedikt Helgason, Cecilia Persson, Yijun Zhou, Stephen J. Ferguson
Computer Methods and Programs in Biomedicine
Medical Imaging and Analysis
article

Optimization of primary screw stability in Trabecular bone using neural network-based models

Benedikt Helgason, Cecilia Persson, Yijun Zhou, Stephen J. Ferguson
article en

Abstract

Multiple linear regression models were found to be insufficient for generating optimized screw configurations, and more complex surrogate models, such as NN, are needed. It could be concluded that different trabecular bone morphologies can benefit from distinct optimal screw designs. The insights gained from this study could have implications for the development of patient-specific orthopaedic treatments.

Computer Methods and Programs in BiomedicineVol. 264
Uppsala University (SE), ETH Zurich (CH), Institute for Biomedical Engineering (CH)
Horizon 2020, H2020 Marie Skłodowska-Curie Actions
Openalex Percentile: Top 96%
Medical Imaging and Analysis
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Optimization of primary screw stability in Trabecular bone using neural network-based models — Benedikt Helgason, Cecilia Persson, et al. · Computer Methods and Programs in Biomedicine (2025) | TGRS Research Map | TGRS