Evidence map›Paper›PMID 36085434›Full record

ArticleInternational journal of computer assisted radiology and surgery2022

Semi-automatic method for pre-surgery scoliosis classification on X-ray images using Bending Asymmetry Index.

D Yang, T T Y Lee, K K L Lai, T P Lam, R M Castelein, J C Y Cheng, Yong Ping Zheng

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Article in International journal of computer assisted radiology and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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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.

2 · The registry

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

D YangDepartment of Biomedical Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China.
T T Y LeeDepartment of Biomedical Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China.
K K L LaiDepartment of Biomedical Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China.
T P LamSH Ho Scoliosis Research Lab, Joint Scoliosis Research Center of the Chinese University of Hong Kong and Nanjing University, Department of Orthopaedics and Traumatology, The Chinese University of Hong Kong, Shatin, Hong Kong.
R M CasteleinDepartment of Orthopaedic Surgery, University Medical Center Utrecht, Utrecht, Netherlands.
J C Y ChengSH Ho Scoliosis Research Lab, Joint Scoliosis Research Center of the Chinese University of Hong Kong and Nanjing University, Department of Orthopaedics and Traumatology, The Chinese University of Hong Kong, Shatin, Hong Kong.
Yong Ping ZhengDepartment of Biomedical Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China. yongping.zheng@polyu.edu.hk.ORCID http://orcid.org/0000-0002-3407-9226

Funding

Health and Medical Research Fund of Hong Kong 04152896Hong Kong Research Grant Council R5017-18
6 · The paper itself

Abstract

purposeBending Asymmetry Index (BAI) has been proposed to characterize the types of scoliotic curve in three-dimensional ultrasound imaging. Scolioscan has demonstrated its validity and reliability in scoliosis assessment with manual assessment-based X-ray imaging. The objective of this study is to investigate the ultrasound-derived BAI method to X-ray imaging of scoliosis, with supplementary information provided for the pre-surgery planning.

methodsAbout 30 pre-surgery scoliosis subjects (9 males and 21 females; Cobb: 50.9 ± 19.7°, range 18°-115°) were investigated retrospectively. Each subject underwent three-posture X-ray scanning supine on a plain mattress on the same day. BAI is an indicator to distinguish structural or non-structural curves through the spine flexibility information obtained from lateral bending spinal profiles. BAI was calculated semi-automatically with manual annotation of vertebral centroids and pelvis level inclination adjustment. BAI classification was validated with the scoliotic curve type and traditional Lenke classification using side-bending Cobb angle measurement (S-Cobb).

results82 curves from 30 pre-surgery scoliosis patients were included. The correlation coefficient was R

conclusionBAI method has demonstrated its inter-modality versatility in X-ray imaging application. The curve type classification and the pre-surgery Lenke classification both indicated promising performances upon the exploratory dataset. A fully-automated of BAI measurement is surely an interesting direction to continue our endeavor. Deep learning on the vertebral-level segmentation should be involved in further study.

Indexed as

ScoliosisFemaleHumansMaleReproducibility of ResultsRetrospective StudiesSpineThoracic VertebraeX-RaysBAIBending Asymmetry IndexBending X-rayLenke classificationScoliosis type

Identifiers

PMID36085434

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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.