Automated Quantification of Deformities in Congenital Radio-Ulnar SynostosisAutomated Method for quantifying CRUS

Ying Cui, Tianfeng Zhou*, Lu Liu, Shanlin Chen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Congenital Radio-Ulnar Synostosis (CRUS) is complex and difficult to quantify. To assist the diagnosis of the CRUS, an automated quantification method of CRUS was studied. Twenty children with CRUS were included in the study. Half of these children suffered from unilateral deformities, while others suffered from bilateral deformities. The local coordinate systems were established based on the recognized forearm landmarks and bone shaft axes, and then the deformity angles were quantified automatically. The reliability was varified by comparing with "Global Truth"labeled by professional surgeons. T-test was conducted to analyse the influence of CRUS on the deformity angles and radio-ulnar length ratio. Spearman correlation analysis was performed to explore the relationship between the level of forearm fusion and the patient age. There is no statistically significant differences between the quantitative results obtained by automated methods and "Global Truth"according to the results of T-test analysis. By quantitative analysis on forearm with or without CRUS, more attention should be paid to the radial and dorsal and the internal rotation angle of the radius (RAR, DAR, and IRAR, respectively), as well as the internal rotation angle of ulna (IRAU). Appropriate plan for wedge osteotomy should be prespecified due to the short ulna, which is common in CRUS. The automated quantification method can assist clinical diagnosis and the quantification of CRUS, which is expected to guide the osteotomy preoperative planing.

Original languageEnglish
Title of host publicationProceedings of 2023 4th International Symposium on Artificial Intelligence for Medicine Science, ISAIMS 2023
PublisherAssociation for Computing Machinery
Pages509-516
Number of pages8
ISBN (Electronic)9798400708138
DOIs
Publication statusPublished - 20 Oct 2023
Event4th International Symposium on Artificial Intelligence for Medicine Science, ISAIMS 2023 - Hybrid, Chengdu, China
Duration: 20 Oct 202323 Oct 2023

Publication series

NameACM International Conference Proceeding Series

Conference

Conference4th International Symposium on Artificial Intelligence for Medicine Science, ISAIMS 2023
Country/TerritoryChina
CityHybrid, Chengdu
Period20/10/2323/10/23

Keywords

  • Congenital Radio-Ulnar Synostosis
  • automated diagnosis
  • forearm deformity

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