Image-based modelling for adolescent idiopathic scoliosis: mechanistic machine learning analysis and prediction

DOI10.1016/j.cma.2020.113590zbMath1506.92016OpenAlexW3112374594MaRDI QIDQ2021272

Ayesha Maqsood, Yongjie Jessica Zhang, Aishwarya Pawar, Sourav Saha, Farzam Tajdari, Mahsa Tajdari, John F. Sarwark, Emmett Cleary, Hengyang Li, Wing Kam Liu

Publication date: 26 April 2021

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cma.2020.113590

zbMATH Keywords

patient-specific geometrypredictive modelsX-ray imagesadolescent idiopathic scoliosis of the human spinemechanistic machine learningsurrogate finite element and bone growth models

Mathematics Subject Classification ID

Biomedical imaging and signal processing (92C55) Finite element, Rayleigh-Ritz and Galerkin methods for boundary value problems involving PDEs (65N30) Pathology, pathophysiology (92C32)

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Uses Software

• DTHB3D_Reg

Cites Work

• Adaptive FEM-based nonrigid image registration using truncated hierarchical B-splines
• Hierarchical deep learning neural network (HiDeNN): an artificial intelligence (AI) framework for computational science and engineering
• Geometric Modeling and Mesh Generation from Scanned Images
• DTHB3D_Reg: Dynamic Truncated Hierarchical B-Spline Based 3D Nonrigid Image Registration
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