ArticleMedical engineering & physics2024
Automated 2D and 3D finite element overclosure adjustment and mesh morphing using generalized regression neural networks.
Article in Medical engineering & physics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Rapid personalized computational modeling of the wrist.Medical engineering & physics · 2026Article
- Mechanochemical modeling of exercise-induced skeletal muscle hypertrophy.PLoS computational biology · 2026Article
- Artificial Intelligence Models for Classifying Wrist Ligament Injuries Using Synthetically-Generated Joint Proximity Maps from Finite Element Models.bioRxiv : the preprint server for biology · 2026Article
- Finite Element Modeling of the Scaphoid Shift Maneuver: Implications for Scapholunate Ligament injuries.bioRxiv : the preprint server for biology · 2026Article
- Contributions of Muscle Forces to Stability and Mobility in Radiography-Driven Models of Total Shoulder Arthroplasty.Journal of biomechanical engineering · 2025Article
- Four-dimensional computed tomography-derived distal radioulnar joint osteokinematics during pronosupination with unilateral triangular fibrocartilage complex injuries.Journal of biomechanics · 2025Article
- Validating subject-specific knee models fromFrontiers in bioengineering and biotechnology · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Computer representations of three-dimensional (3D) geometries are crucial for simulating systems and processes in engineering and science. In medicine, and more specifically, biomechanics and orthopaedics, obtaining and using 3D geometries is critical to many workflows. However, while many tools exist to obtain 3D geometries of organic structures, little has been done to make them usable for their intended medical purposes. Furthermore, many of the proposed tools are proprietary, limiting their use. This work introduces two novel algorithms based on Generalized Regression Neural Networks (GRNN) and 4 processes to perform mesh morphing and overclosure adjustment. These algorithms were implemented, and test cases were used to validate them against existing algorithms to demonstrate improved performance. The resulting algorithms demonstrate improvements to existing techniques based on Radial Basis Function (RBF) networks by converting to GRNN-based implementations. Implementations in MATLAB of these algorithms and the source code are publicly available at the following locations: https://github.com/thor-andreassen/femors; https://simtk.org/projects/femors-rbf; https://www.mathworks.com/matlabcentral/fileexchange/120353-finite-element-morphing-overclosure-reduction-and-slicing.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.