Study on the Utilization of AI Recognition Software for DICOM Format CT Data in Ruedi-Augower Ⅱ Type Pilon Fractures

YIN Xiaodong; CHENG Yongzhong; CHEN Yang; YAN Wei; SUN Haibin; LI Ke; ZENG Fangao

doi:10.3969/j.issn.1674-1633.20241682

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China Medical Devices ›› 2025, Vol. 40 ›› Issue (2) : 130-136. DOI: 10.3969/j.issn.1674-1633.20241682

RESEARCH WORK

Study on the Utilization of AI Recognition Software for DICOM Format CT Data in Ruedi-Augower Ⅱ Type Pilon Fractures

YIN Xiaodong^1a, CHENG Yongzhong^1a,2a, CHEN Yang^1a, YAN Wei^1b, SUN Haibin^2b, LI Ke^2b, ZENG Fangao^2b

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Abstract

Objective To evaluate the accuracy of artificial intelligence (AI) software in identifying and localizing Ruedi-Augower type Ⅱ Pilon fractures. Methods A retrospective analysis was conducted on 20 cases of Ruedi-Augower type Ⅱ Pilon fractures with DICOM format CT data collected from January 2020 to August 2024. After random numbering, the data were analyzed by a senior orthopedic surgeon and a junior orthopedic resident using both the hospital’s PACS system and AI software. Once consensus was reached on the fracture fragments requiring reduction, measurements of displacement and rotation angles along the X-axis (mediallateral), Y-axis (superior-inferior), and Z-axis (anterior-posterior) were performed in both systems. Additionally, the AI software was used to provide information on fracture classification and fracture point identification. Results The software achieved an accuracy rate of 70% for fracture classification. No statistically significant differences were observed between the two groups in terms of X, Y, and Z-axis displacements and rotation angles of the fracture fragments (P>0.05). The consistency between the two measurement methods was excellent. Conclusion The performance of the AI software in measuring displacement information for Ruedi-Augower type Ⅱ Pilon fractures is comparable to manual measurement, validating its precision in identification and localization. Its innovative approach to simulating reduction measurements can provide valuable guidance for preoperative planning of reduction strategies.

Key words

artificial intelligence; fracture classification; tibial fractures; computed tomography; software identification; fracture classification; DICOM format

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YIN Xiaodong, CHENG Yongzhong, CHEN Yang, et al. Study on the Utilization of AI Recognition Software for DICOM Format CT Data in Ruedi-Augower Ⅱ Type Pilon Fractures[J]. China Medical Devices, 2025, 40(2): 130-136 https://doi.org/10.3969/j.issn.1674-1633.20241682

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