An automatic congenital radio-ulnar synostosis deformity evaluation method (CRUS-DE): integrating TLT-SAM and GPMM-R for landmark identification.

Lu Liu, Ying Cui, Tianfeng Zhou, Shanlin Chen, Yubing Guo, Xinhua Zhou

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Article in Scientific reports, 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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6 authors.

Lu LiuDepartment of Hand Surgery, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China.

Ying CuiSchool of Mechanical Engineering, Beijing Institute of Technology, Beijing, China.

Tianfeng ZhouSchool of Mechanical Engineering, Beijing Institute of Technology, Beijing, China. zhoutf@bit.edu.cn.

Shanlin ChenDepartment of Hand Surgery, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China.

Yubing GuoSchool of Medical Technology, Beijing Institute of Technology, Beijing, China.

Xinhua ZhouDepartment of Orthopedics, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China.

Funding

Beijing Hospitals Authority's Ascent Plan DFL20240402Beijing Municipal Health Commission BJRITO-RDP-2024

6 · The paper itself

Abstract

Congenital Radio-Ulnar Synostosis (CRUS) causes difficulty in forearm rotation, which is treated using osteotomy. Effective preoperative planning of osteotomy requires automatic and objective quantification of deformity angles. In this paper, an automatic Congenital Radio-Ulnar Synostosis deformity evaluation method (CRUS-DE) is proposed. Initially, a method using threshold-layer tracking (TLT) and the segment anything model (SAM) is designed to recognize and segment the forearm from CT images. Subsequently, the Gaussian Process Morphable Model, in conjunction with refinement based on anatomical characteristics is developed to accurately identify forearm landmarks. Finally, the model automatically estimates deformity angles for quantitative assessment of CRUS. The forearm landmarks and deformity angles were successfully obtained from CT images based on CRUS-DE with average errors ranging from 0.98 to 1.55 mm and from 0.7° to 2.4°, respectively. No significant differences existed between the automatic method and manual method. The CRUS-DE, as an explainable method, presented excellent performance in the quantification of deformity angles. This method can be used in preoperative planning and postoperative evaluation of osteotomy for forearm deformities.

Indexed as

RadiusSynostosisUlnaAnatomic LandmarksFemaleForearmHumansImage Processing, Computer-AssistedMaleOsteotomyTomography, X-Ray ComputedAutomatic deformity quantificationCongenital radio-ulnar synostosis (CRUS)CT imageForearm landmarks identificationOsteotomy preoperative planning

Identifiers

PMID41606024

DOI10.1038/s41598-026-36638-4

PMCPMC12910016

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