Next-generation prognosis framework for pediatric spinal deformities using bio-informed deep learning networks

Next-generation prognosis framework for pediatric spinal deformities using bio-informed deep learning networks

Tajdari, Mahsa (Northwestern University)
Tajdari, F. (TU Delft Emerging Materials)
Shirzadian, Pouyan (Virginia Tech)
Pawar, Aishwarya (Purdue University)
Wardak, Mirwais (Stanford University School of Medicine)
Saha, Sourav (Northwestern University)
Huysmans, T. (TU Delft Applied Ergonomics and Design)
Song, Y. (TU Delft Emerging Materials)
Zhang, Yongjie Jessica (Carnegie Mellon University)

2022

Predicting pediatric spinal deformity (PSD) from X-ray images collected on the patient’s initial visit is a challenging task. This work builds on our previous method and provides a novel bio-informed framework based on a mechanistic machine learning technique with dynamic patient-specific parameters to predict PSD. We provide a geometry-based bone growth model that can be utilized in a range of applications to enhance the bio-informed mechanistic machine learning framework. The proposed technique is utilized to examine and predict spine curvature in PSD cases such as adolescent idiopathic scoliosis. The best fit of a segmented 3D volumetric geometry of the human spine acquired from 2D X-ray images is employed. Using an active contour model based on gradient vector flow snakes, the anteroposterior and lateral views of the X-ray images are segmented to derive the 2D contours surrounding each vertebra. Using minimal user input, the snake parameters are calibrated and automatically computed over the dataset, resulting in fast image segmentation and data collection. The 2D segmented outlines of each vertebra are transformed into a 3D image segmentation result. The Iterative Closest Point mesh registration technique is then used to establish a mesh morphing approach and creates a 3D atlas spine model. Using the comprehensive 3D volumetric model, one can automatically extract spinal geometry data as inputs to the mechanistic machine learning network. Moreover, the proposed bio-informed deep learning network with the modified bone growth model achieves competitive or even superior performance against other state-of-the-art learning-based methods.Please check and confirm if the author names and initials are correct for “Yongjie Jessica Zhang” and “Wing Kam Liu”.We confirm they are correct.

3D reconstruction
Adolescent idiopathic scoliosis
Dynamic model
Mechanistic bio-informed machine learning
Mesh registration
Pediatric spinal deformity
X-ray images

http://resolver.tudelft.nl/uuid:fb775e78-6fb1-42cc-a604-283d5f0b5f35

https://doi.org/10.1007/s00366-022-01742-2

2023-07-01

0177-0667

Engineering with Computers: an international journal of simulation-based engineering, 38 (5), 4061-4084

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2022 Mahsa Tajdari, F. Tajdari, Pouyan Shirzadian, Aishwarya Pawar, Mirwais Wardak, Sourav Saha, T. Huysmans, Y. Song, Yongjie Jessica Zhang, More Authors