TY - JOUR
T1 - Image-based modelling for Adolescent Idiopathic Scoliosis
T2 - Mechanistic machine learning analysis and prediction
AU - Tajdari, Mahsa
AU - Pawar, Aishwarya
AU - Li, Hengyang
AU - Tajdari, Farzam
AU - Maqsood, Ayesha
AU - Cleary, Emmett
AU - Saha, Sourav
AU - Zhang, Yongjie Jessica
AU - Sarwark, John F.
AU - Liu, Wing Kam
N1 - Funding Information:
We would like to thank the Division of Orthopaedic Surgery and Sports Medicine at Ann and Robert H. Lurie Children’s Hospital for their collaboration on this project made possible by a philanthropic grant. W.K. Liu, S. Saha, and H. Li would like to acknowledge the support of National Science Foundation (NSF) CMMI-1762035 . M. Tajdari would like to thank Sarah Ziselman for her assistance in data processing. A. Pawar and Y.J. Zhang were supported in part by the NSF grants CMMI-1953323 , CBET-1804929 and a PITA (Pennsylvania Infrastructure Technology Alliance) grant. Moreover, F. Tajdari is partly supported by Dutch NWO Next UPPS — Integrated design methodology for Ultra Personalized Products and Services project.
Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2021/2/1
Y1 - 2021/2/1
N2 - Scoliosis, an abnormal curvature of the human spinal column, is characterized by a lateral deviation of the spine, accompanied by axial rotation of the vertebrae. Adolescent Idiopathic Scoliosis (AIS) is the most common type, affecting children between ages 8 to 18 when bone growth is at its maximum rate. We propose a mechanistic machine learning algorithm in order to study patient-specific AIS curve progression, which is associated with the bone growth and other genetic and environmental factors. Two different frameworks are used to analyse and predict curve progression, one with implementing clinical data extracted from 2D X-ray images and the other one with incorporating both clinical data and physical equations governing the non-uniform bone growth. The physical equations governing bone growth are affiliated with calculating all stress components at each region. The stress values are evaluated through a surrogate finite element simulation and a bone growth model on a detailed patient-specific geometry of the human spine. We also propose a patient-specific framework to generate the volumetric model of human spine which is partitioned into different tissues for both vertebra and intervertebral disc. It is shown that implementing physical equations governing bone growth into the prediction framework will notably improve the prediction results as compared to only using clinical data for prediction. In addition, we can predict curve progression at ages outside the range of training samples.
AB - Scoliosis, an abnormal curvature of the human spinal column, is characterized by a lateral deviation of the spine, accompanied by axial rotation of the vertebrae. Adolescent Idiopathic Scoliosis (AIS) is the most common type, affecting children between ages 8 to 18 when bone growth is at its maximum rate. We propose a mechanistic machine learning algorithm in order to study patient-specific AIS curve progression, which is associated with the bone growth and other genetic and environmental factors. Two different frameworks are used to analyse and predict curve progression, one with implementing clinical data extracted from 2D X-ray images and the other one with incorporating both clinical data and physical equations governing the non-uniform bone growth. The physical equations governing bone growth are affiliated with calculating all stress components at each region. The stress values are evaluated through a surrogate finite element simulation and a bone growth model on a detailed patient-specific geometry of the human spine. We also propose a patient-specific framework to generate the volumetric model of human spine which is partitioned into different tissues for both vertebra and intervertebral disc. It is shown that implementing physical equations governing bone growth into the prediction framework will notably improve the prediction results as compared to only using clinical data for prediction. In addition, we can predict curve progression at ages outside the range of training samples.
KW - Adolescent idiopathic scoliosis of the human spine
KW - Mechanistic machine learning
KW - Patient-specific geometry
KW - Predictive models
KW - Surrogate finite element and bone growth models
KW - X-ray images
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U2 - 10.1016/j.cma.2020.113590
DO - 10.1016/j.cma.2020.113590
M3 - Article
AN - SCOPUS:85097579087
SN - 0374-2830
VL - 374
JO - Computer Methods in Applied Mechanics and Engineering
JF - Computer Methods in Applied Mechanics and Engineering
M1 - 113590
ER -