| 人机交互CT影像AI识别定位技术在C1型桡骨远端骨折的初步应用 |
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投稿时间：2024-09-10
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| 作者 | Author | 单位 | Address | E-Mail |
| 成永忠 |
CHENG Yong-zhong |
中国中医科学院望京医院, 北京 100102
南阳市中医院, 河南 南阳 473000 |
Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing 100102, China
Nanyang City Traditional Chinese Medicine Hospital, Nanyang 473000, Henan, China |
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| 尹晓冬 |
YIN Xiao-dong |
中国中医科学院望京医院, 北京 100102 |
Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing 100102, China |
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| 刘飞 |
LIU Fei |
中国中医科学院望京医院, 北京 100102 |
Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing 100102, China |
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| 邓新恒 |
DENG Xin-heng |
南阳市中医院, 河南 南阳 473000 |
Nanyang City Traditional Chinese Medicine Hospital, Nanyang 473000, Henan, China |
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| 王朝鲁 |
WANG Chao-lu |
中国中医科学院望京医院, 北京 100102
南阳市中医骨伤生物力学重点实验室, 河南 南阳 473000 |
Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing 100102, China
Nanyang City Traditional Chinese Medicine Orthopedic Biomechanics Key Laboratory, Nanyang 473000, Henan, China |
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| 崔书克 |
CUI Shu-ke |
南阳市中医院, 河南 南阳 473000
河南省张仲景经方智能化开发工程技术研究中心, 河南 南阳 473000 |
Nanyang City Traditional Chinese Medicine Hospital, Nanyang 473000, Henan, China
ZHANG Zhongjing Prescription Intelligent Development Engineering Technology Research Center of Henan Province, Nanyang 473000, Henan, China |
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| 李永耀 |
LI Yong-yao |
中国中医科学院望京医院, 北京 100102 |
Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing 100102, China |
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| 闫威 |
YAN Wei |
中国中医科学院望京医院, 北京 100102 |
Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing 100102, China |
yw285858404@sina.com |
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| 期刊信息：《中国骨伤》2025年，第38卷，第1期，第31-40页 |
| DOI：10.12200/j.issn.1003-0034.20240601 |
| 基金项目:中国中医科学院科技创新工程(编号:CI2021A02008);首都临床特色诊疗技术研究及转化应用项目(编号:Z221100007422075);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(编号:WJYY-XZKT-2023-14) |
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中文摘要:
目的: 探讨人机交互智能软件识别定位C1型桡骨远端骨折的精准性。
方法: 回顾性分析2023年9月至2024年3月收治的14例C1型桡骨远端骨折患者的CT数据，其中男3例，女11例，年龄27~82岁，随机编号后，由1名高年资骨科医师在院内影像系统上阅片并测量每例患者的尺偏角、桡骨高度、掌倾角、关节内台阶、关节内间隙等，依据桡骨远端骨折复位标准，分为复位组、非复位组。随后将数据依次导入人机交互智能软件，由1名低年资骨科医师识别分析，并测量出同人工测量同样的指标，依据同样标准分组后发现与人工测量信息一致(均复位组6例、非复位组8例，且组内数据两者一致)。继续在软件中对非复位数据行骨骼分割、骨折识别等处理，并生成包含骨折识别信息的诊断报告8份。最后针对需要复位的6例数据，由高年资医师和低年资医师分别在院内影像系统和软件中识别分析，分别判断出每例数据中需要复位的骨折块，由另2名高年资骨科医师核实两者所识别的骨块一致后，再进行骨块位移信息测量;两者在各自系统中分别测量出每1骨块在X轴(内外)、Z轴(前后)、Y轴(上下)上的位移、旋转角度等信息，同时软件组对数据进行骨折识别，并生成包含所有骨块位移信息、骨折识别信息的诊断报告6份。两者分别将各自数据录入数据表中。
结果: 依据桡骨远端骨折复位标准，两组均得出6例复位、8例非复位数据，且分组数据一致;经配对样本t检验，人工和软件测量14例数据的尺偏角、桡骨高度、关节内台阶、掌倾角、关节内间隙等比较，差异均无统计学意义(P>0.05);在骨折识别方面，软件识别出10例C型骨折，4例数据识别为B型;6例复位数据，每例数据两种测量方式均分割出2个骨块，每组12个骨块，经判定发现所识别骨块一致，经配对样本t检验发现，人工和软件测量骨块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)。
结论: 人机交互CT影像AI识别定位软件在测量C1型桡骨远端骨折解剖学参数方面效能与人工测量近似。 |
| 【关键词】桡骨远端骨折 人机交互 计算机断层扫描 AI识别定位 |
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| Preliminary application of human-computer interaction CT imaging AI recognition and positioning technology in the treatment of type C1 distal radius fractures |
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ABSTRACT
Objective To explore the accuracy of human-computer interaction software in identifying and locating type C1 distal radius fractures.
Methods Based on relevant inclusion and exclusion criteria,14 cases of type C1 distal radius fractures between September 2023 and March 2024 were retrospectively analyzed,comprising 3 males and 11 females(aged from 27 to 82 years). The data were assigned randomized identifiers. A senior orthopedic physician reviewed the films and measured the ulnar deviation angle,radial height,palmar inclination angle,intra-articular step,and intra-articular gap for each case on the hospital's imaging system. Based on the reduction standard for distal radius fractures,cases were divided into reduction group and non-reduction group. Then,the data were sequentially imported into a human-computer interaction intelligent software,where a junior orthopedic physician analyzed the same radiological parameters,categorized cases,and measured fracture details. The categorization results from the software were consistent with manual classifications (6 reduction cases and 8 non-reduction cases). For non-reduction cases,the software performed further analyses,including bone segmentation and fracture recognition,generating 8 diagnostic reports containing fracture recognition information. For the 6 reduction cases,the senior and junior orthopedic physicians independently analyzed the data on the hospital's imaging system and the AI software,respectively. Bone segments requiring reduction were identified,verified by two senior physicians,and measured for displacement and rotation along the X (inward and outward),Z (front and back),and Y (up and down) axes. The AI software generated comprehensive diagnostic reports for these cases,which included all measurements and fracture recognition details.
Results Both the manual and AI software methods consistently categorized the 14 cases into 6 reduction and 8 non-reduction groups,with identical data distributions. A paired sample t-test revealed no statistically significant differences (P>0.05) between the manual and software-based measurements for ulnar deviation angle,radial ulnar bone height,palmar inclination angle,intra-articular step,and joint space. In fracture recognition,the AI software correctly identified 10 C-type fractures and 4 B-type fractures. For the 6 reduction cases,a total of 24 bone fragments were analyzed across both methods. After verification,it was found that the bone fragments identified by the two methods were consistent. A paired sample t-tests revealed that the identified bone fragments and measured displacement and rotation angles along the X,Y,and Z axes were consistent between the two methods. No statistically significant differences(P>0.05) were found between manual and software measurements for these parameters.
Conclusion Human-computer interaction software employing AI technology demonstrated comparable accuracy to manual measurement in identifying and locating type C1 distal radius fractures on CT imaging. |
| KEY WORDS Distal radius fracture Human-Computer interaction Computed tomography(CT) AI-powered geolocation identification |
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| 引用本文，请按以下格式著录参考文献： |
| 中文格式: | 成永忠,尹晓冬,刘飞,邓新恒,王朝鲁,崔书克,李永耀,闫威.人机交互CT影像AI识别定位技术在C1型桡骨远端骨折的初步应用[J].中国骨伤,2025,38(1):31~40 |
| 英文格式: | CHENG Yong-zhong,YIN Xiao-dong,LIU Fei,DENG Xin-heng,WANG Chao-lu,CUI Shu-ke,LI Yong-yao,YAN Wei.Preliminary application of human-computer interaction CT imaging AI recognition and positioning technology in the treatment of type C1 distal radius fractures[J].zhongguo gu shang / China J Orthop Trauma ,2025,38(1):31~40 |
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