Evidence map›Paper›PMID 39486021›Full record

ArticleJournal of medical Internet research2024

Use of Artificial Intelligence in Cobb Angle Measurement for Scoliosis: Retrospective Reliability and Accuracy Study of a Mobile App.

Haodong Li, Chuang Qian, Weili Yan, Dong Fu, Yiming Zheng, Zhiqiang Zhang, Junrong Meng, Dahui Wang

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Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

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11citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Haodong Li *Department of Orthopedics, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.ORCID 0009-0000-5822-7353
Chuang Qian *Department of Orthopedics, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.ORCID 0000-0003-2375-3614
Weili Yan *Department of Orthopedics, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.ORCID 0000-0002-7633-7449
Dong FuDepartment of Orthopedics, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.ORCID 0000-0002-1585-504X
Yiming ZhengDepartment of Orthopedics, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.ORCID 0000-0002-7947-2769
Zhiqiang ZhangDepartment of Orthopedics, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.ORCID 0000-0001-7698-7655
Junrong MengDepartment of Orthopedics, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.ORCID 0000-0001-7220-2996
Dahui WangDepartment of Orthopedics, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.ORCID 0000-0002-3101-702X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundScoliosis is a spinal deformity in which one or more spinal segments bend to the side or show vertebral rotation. Some artificial intelligence (AI) apps have already been developed for measuring the Cobb angle in patients with scoliosis. These apps still require doctors to perform certain measurements, which can lead to interobserver variability. The AI app (cobbAngle pro) in this study will eliminate the need for doctor measurements, achieving complete automation.

objectiveWe aimed to evaluate the reliability and accuracy of our new AI app that is based on deep learning to automatically measure the Cobb angle in patients with scoliosis.

methodsA retrospective analysis was conducted on the clinical data of children with scoliosis who were treated at the Pediatric Orthopedics Department of the Children's Hospital affiliated with Fudan University from July 2019 to July 2022. Three measurers used the Picture Archiving and Communication System (PACS) to measure the coronal main curve Cobb angle in 802 full-length anteroposterior and lateral spine X-rays of 601 children with scoliosis, and recorded the results of each measurement. After an interval of 2 weeks, the mobile AI app was used to remeasure the Cobb angle once. The Cobb angle measurements from the PACS were used as the reference standard, and the accuracy of the Cobb angle measurements by the app was analyzed through the Bland-Altman test. The intraclass correlation coefficient (ICC) was used to compare the repeatability within measurers and the consistency between measurers.

resultsAmong 601 children with scoliosis, 89 were male and 512 were female (age range: 10-17 years), and 802 full-length spinal X-rays were analyzed. Two functionalities of the app (photography and photo upload) were compared with the PACS for measuring the Cobb angle. The consistency was found to be excellent. The average absolute errors of the Cobb angle measured by the photography and upload methods were 2.00 and 2.08, respectively. Using a clinical allowance maximum error of 5°, the 95% limits of agreement (LoAs) for Cobb angle measurements by the photography and upload methods were -4.7° to 4.9° and -4.9° to 4.9°, respectively. For the photography and upload methods, the 95% LoAs for measuring Cobb angles were -4.3° to 4.6° and -4.4° to 4.7°, respectively, in mild scoliosis patients; -4.9° to 5.2° and -5.1° to 5.1°, respectively, in moderate scoliosis patients; and -5.2° to 5.0° and -6.0° to 4.8°, respectively, in severe scoliosis patients. The Cobb angle measured by the 3 observers twice before and after using the photography method had good repeatability (P<.001). The consistency between the observers was excellent (P<.001).

conclusionsThe new AI platform is accurate and repeatable in the automatic measurement of the Cobb angle of the main curvature in patients with scoliosis.

Indexed as

Artificial IntelligenceMobile ApplicationsScoliosisAdolescentChildFemaleHumansMaleReproducibility of ResultsRetrospective Studiesartificial intelligencedeep learningphotogrammetryscoliosis

Identifiers

PMID39486021
PMCPMC11568394

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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.