Evidence map›Paper›PMID 41537944›Full record

ArticleSpine deformity2026

Automatic radiation-free evaluation of Cobb angle for spinal curvature based on fringe projection profilometry and deep learning technology.

Chi-Kuang Feng, Ching-Jung Hung, Yen-Ju Chen, Pei-Yu Su, Guan-Ting Liu, Cheng-Yang Liu

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Spine deformity, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Chi-Kuang FengDepartment of Orthopaedics and Traumatology, Taipei Veterans General Hospital, Taipei, Taiwan.
Ching-Jung HungDepartment of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Yen-Ju ChenDepartment of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Pei-Yu SuDepartment of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Guan-Ting LiuDepartment of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Cheng-Yang LiuDepartment of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan. cyliu66@nycu.edu.tw.ORCID http://orcid.org/0000-0002-4035-8686

Funding

National Science and Technology Council 111-2221-E-A49-102-MY2National Science and Technology Council 113-2223-E-007-005the Veterans General Hospitals and University System of Taiwan Joint Research Program VGHUST112-G7-6-1the Veterans General Hospitals and University System of Taiwan Joint Research Program VGHUST113-G7-5-1the Veterans General Hospitals and University System of Taiwan Joint Research Program VGHUST114-G7-4-1
6 · The paper itself

Abstract

objectivePediatric scoliosis is the most prevalent spinal disorder, often leading to abnormal curvature and deformation of the spine. Early detection is essential for timely intervention, particularly in growing adolescents. In this study, we present a novel, fully automated, radiation-free method for Cobb angle evaluation, combining fringe projection profilometry with deep learning technologies. MATERIALS AND

methodsA three-dimensional reconstruction of the participant's back surface is achieved using a seven-step phase-shifting algorithm. Convolutional neural networks are then utilized to extract asymmetry features from the 3D surface and predict the Cobb angle, a key clinical indicator of scoliosis severity. A total of 48 participants clinically diagnosed with scoliosis based on radiographic imaging were recruited from the hospital.

resultsThe experimental results demonstrate a strong correlation between the predicted and actual Cobb angles, with a correlation coefficient of 0.94 and a coefficient of determination of 0.8796 during Adam's forward bend test. The mean time required from scanning to Cobb angle prediction is approximately 3.3 s.

conclusionsThe proposed evaluation method exhibits excellent discriminative capability and shows significant potential as an alternative to the traditional scoliometer for large-scale Cobb angle screening programs in schools.

Indexed as

Deep LearningImaging, Three-DimensionalScoliosisAdolescentAlgorithmsChildConvolutional Neural NetworksFemaleHumansMaleDeep learningFringe projectionIdiopathic scoliosisNoninvasive detection

Identifiers

PMID41537944
PMCPMC13282249

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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.