Evidence map›Paper›PMID 41910750›Full record

ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2026

Intraoperative full-spine imaging with C-arm using a deep learning-based automatic stitching model: development and clinical validation.

Yibing Liu, Guomao Zhu, Zhenzhuo Zhang, Jinqian Liang

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Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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5 · Who and what money

Authors and funding

4 authors.

Yibing LiuOrthopedics Department, Peking Union Medical College Hospital, No.1 Shuaifuyuan, Dongcheng District, 100730, Beijing, China.
Guomao ZhuOrthopedics Department, Peking Union Medical College Hospital, No.1 Shuaifuyuan, Dongcheng District, 100730, Beijing, China.
Zhenzhuo ZhangOrthopedics Department, Peking Union Medical College Hospital, No.1 Shuaifuyuan, Dongcheng District, 100730, Beijing, China.
Jinqian LiangOrthopedics Department, Peking Union Medical College Hospital, No.1 Shuaifuyuan, Dongcheng District, 100730, Beijing, China. 18701200507@163.com.

Funding

National High Level Hospital Clinical Research Funding 2022-PUMCH-B-002)National Natural Science Foundation of China 82072477
6 · The paper itself

Abstract

purposeTo develop and clinically validate an automated deep learning–based stitching model for intraoperative generation of full-length spinal radiographs from segmented C-arm fluoroscopy during scoliosis surgery.

methodsA deep learning model was developed to detect pedicle screws and align sequential intraoperative C-arm images to reconstruct a full-length spine view. For clinical validation, we retrospectively analyzed 43 scoliosis patients (2018–2023) with adequate intraoperative segmented sequences. Stitched intraoperative images (Method A) were compared with postoperative full-length standing digital radiographs as the reference standard (Method B). Five spine surgeons independently measured coronal Cobb angles on both image sets. Interobserver reliability was assessed using intraclass correlation coefficients (ICC). Agreement between methods was evaluated using ICC, Bland–Altman analysis, and absolute paired differences.

resultsThe model generated stitched full-length images within 5 s. The mean absolute Cobb angle difference between Methods A and B was 1.95°(SD 2.81°), with a median of 0.20°(IQR 0.00–2.80°) and a range of 0.00–9.80°. Absolute differences were ≤ 1°, ≤ 3°, and ≤ 5° in 76.74%, 88.37%, and 97.67% of paired measurements. Interobserver reliability was excellent (ICC 0.999 for Method A; 0.998 for Method B). Between-method agreement was high (ICC 0.991) with minimal bias (mean difference − 0.13°, 95% limits of agreement − 4.20° to 3.93°).

conclusionAutomated stitching of routine intraoperative C-arm images can rapidly produce full-length spinal radiographs with excellent Cobb angle agreement versus postoperative DR, supporting intraoperative alignment assessment.

Indexed as

C-arm system fluoroscopyDeep learningFull-length spinal imageIntraoperative stitching technology.

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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.