Ruedi-Augower Ⅱ型Pilon骨折DICOM格式CT数据的AI识别软件应用研究

尹晓冬, 成永忠, 陈洋, 闫威, 孙海滨, 李克, 曾凡高

中国医疗设备 ›› 2025, Vol. 40 ›› Issue (2) : 130-136.

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中国医疗设备 ›› 2025, Vol. 40 ›› Issue (2) : 130-136. DOI: 10.3969/j.issn.1674-1633.20241682
研究论著

Ruedi-Augower Ⅱ型Pilon骨折DICOM格式CT数据的AI识别软件应用研究

  • 尹晓冬1a, 成永忠1a, 2a, 陈洋1a, 闫威1b, 孙海滨2b, 李克2b, 曾凡高2b
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Study on the Utilization of AI Recognition Software for DICOM Format CT Data in Ruedi-Augower Ⅱ Type Pilon Fractures

  • YIN Xiaodong1a, CHENG Yongzhong1a,2a, CHEN Yang1a, YAN Wei1b, SUN Haibin2b, LI Ke2b, ZENG Fangao2b
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摘要

目的 探讨人工智能(Artificial Intelligence,AI)软件识别定位Ruedi-Augower Ⅱ型Pilon骨折的精准性。方法 回顾性纳入2020年1月至2024年8月Ruedi-AugowerⅡ型Pilon骨折DICOM格式CT数据20例,随机编号后,分别由1名骨科高年资医师和低年资医师在院内PACS系统和AI软件中识读分析,两者识别出需要复位的骨折块并经判定一致后,在不同系统中测量出每一骨折块在X轴(内外)、Y轴(上下)、Z轴(前后)上的位移、旋转角度,同时用AI软件得出骨折分型和骨折点信息。结果 软件识别骨折分型准确率为70%;2组测量骨折块X、Y、Z轴位移、旋转角度等比较差异均无统计学意义(P>0.05),且2种测量方法的一致性较好。结论 AI软件测量Ruedi-AugowerⅡ型Pilon骨折位移信息效能与人工一致,验证了其识别定位的精准性;其模拟复位测量的方式具有开创性,可为术前规划复位方案提供指导。

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.

关键词

人工智能;骨折分型;胫骨骨折;计算机断层扫描(CT);软件识别;骨折分型;DICOM格式

Key words

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

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导出引用
尹晓冬, 成永忠, 陈洋, . Ruedi-Augower Ⅱ型Pilon骨折DICOM格式CT数据的AI识别软件应用研究[J]. 中国医疗设备, 2025, 40(2): 130-136 https://doi.org/10.3969/j.issn.1674-1633.20241682
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
中图分类号: R44    R741   

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基金

中国中医科学院望京医院高水平中医医院建设项目中医药临床循证研究专项(WJYY-XZKT-2023-02);国家自然科学基金面上项目(82274561)。

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