Image-based modelling for adolescent idiopathic scoliosis: mechanistic machine learning analysis and prediction
type: Item
instance of: scholarly article
title: Image-based modelling for adolescent idiopathic scoliosis: mechanistic machine learning analysis and prediction
zbMATH Open document ID: 1506.92016
author: Mahsa Tajdari
author: Aishwarya Pawar
author: Hengyang Li
author: Farzam Tajdari
author: Ayesha Maqsood
author: Emmett Cleary
author: Sourav Saha
author: Yong-jie Zhang
author: John F. Sarwark
author: Wing K. Liu
published in: Computer Methods in Applied Mechanics and Engineering
publication date: 2021-04-26T00:00:00Z
Mathematics Subject Classification ID: 92C32
Mathematics Subject Classification ID: 92C55
Mathematics Subject Classification ID: 65N30
zbMATH DE Number: 7338029
zbMATH Keywords: adolescent idiopathic scoliosis of the human spine
zbMATH Keywords: X-ray images
zbMATH Keywords: patient-specific geometry
zbMATH Keywords: surrogate finite element and bone growth models
zbMATH Keywords: predictive models
zbMATH Keywords: mechanistic machine learning
describes a project that uses: DTHB3D_Reg
MaRDI profile type: Publication
full work available at URL: j.cma.2020.113590
OpenAlex ID: W3112374594
cites work: Hierarchical deep learning neural network (HiDeNN): an artificial intelligence (AI) framework for computational science and engineering
cites work: Geometric modeling and mesh generation from scanned images
cites work: DTHB3D_Reg: Dynamic Truncated Hierarchical B-Spline Based 3D Nonrigid Image Registration
cites work: Adaptive FEM-based nonrigid image registration using truncated hierarchical B-splines
cites work: Q4148902
cites work: Q4066417
DOI: 10.1016/J.CMA.2020.113590
description: scientific article; zbMATH DE number 7338029