Construction and Clinical Validation of a Deep Learning-Based Automatic Measurement Model for Palmar Tilt and Radial Inclination in Distal Radius Fractures

DAI Guoda; WANG Jianwei; WU Mao; KANG Bin; SHAO Yang; CUI Hengyan; LI Shaoshuo; ZHU Tingchen; HUA Zhen; SHEN Zhongming; LIU Jintao; ZHOU Ming

doi:10.13288/j.11-2166/r.2026.10.011

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Construction and Clinical Validation of a Deep Learning-Based Automatic Measurement Model for Palmar Tilt and Radial Inclination in Distal Radius Fractures

更新时间：2026-05-19

- Construction and Clinical Validation of a Deep Learning-Based Automatic Measurement Model for Palmar Tilt and Radial Inclination in Distal Radius Fractures
- Journal of Traditional Chinese Medicine Vol. 67, Issue 10, Pages: 1093-1100(2026)
- 作者机构：
  
  1.无锡市中医医院，江苏省无锡市中南西路8号，214071
  2.南京邮电大学
  3.苏州市中医医院
  4.常州市中医院
- 作者简介：
- 基金信息：
- DOI：10.13288/j.11-2166/r.2026.10.011
  CLC：
- Received：27 November 2025，
  
  Revised：2026-04-29，
  
  Accepted：08 April 2026，
  
  Published：17 May 2026
- 稿件说明：
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戴国达,王建伟,吴毛等.基于深度学习的桡骨远端骨折掌倾角与尺偏角自动测量模型构建及临床验证[J].中医杂志,2026,67(10):1093-1100.

DAI Guoda,WANG Jianwei,WU Mao,et al.Construction and Clinical Validation of a Deep Learning-Based Automatic Measurement Model for Palmar Tilt and Radial Inclination in Distal Radius Fractures[J].Journal of Traditional Chinese Medicine,2026,67(10):1093-1100.
戴国达,王建伟,吴毛等.基于深度学习的桡骨远端骨折掌倾角与尺偏角自动测量模型构建及临床验证[J].中医杂志,2026,67(10):1093-1100. DOI： 10.13288/j.11-2166/r.2026.10.011.

DAI Guoda,WANG Jianwei,WU Mao,et al.Construction and Clinical Validation of a Deep Learning-Based Automatic Measurement Model for Palmar Tilt and Radial Inclination in Distal Radius Fractures[J].Journal of Traditional Chinese Medicine,2026,67(10):1093-1100. DOI： 10.13288/j.11-2166/r.2026.10.011.

摘要

目的

构建适配中医临床场景的掌倾角与尺偏角自动测量模型，并验证其在中医手法整复相关场景中的准确率与效率。

方法

通过江苏省中医优势病种诊疗大数据平台收集2023年9月1日至2024年9月1日于江苏省18家中医医院及中西医结合医院就诊的桡骨远端骨折患者的桡骨远端正、侧位X线片资料。采用基于多尺度特征融合及边缘感知的医学影像分割框架，结合中医骨伤科解剖认知特点优化人工智能（AI）模型特征提取策略，实现骨折区域自动分割与桡骨远端掌倾角、尺偏角测量，并验证评价AI模型测量尺偏角、掌倾角的准确率，比较AI模型与人工测量的使用时间及平均时间增益率。

结果

共收集桡骨远端骨折患者正、侧位X线片15 444张，经预处理后划分为训练集（11 144张，包括正位5066张、侧位6078张）、验证集（3700张，包括正位1840张、侧位1860张）、独立测试集（600张，包括正位300张、侧位300张）。测试集300张正位X线片尺偏角测量中，AI测量与人工测量数据度数误差＜3°、＜5°时，AI测量准确率分别为83%、93%；300张侧位X线片掌倾角测量中，AI测量与人工测量数据度数误差

＜3°、＜5°，AI测量准确率分别达78%、90%。测量50张X线片，对于尺偏角，AI测量使用时间为（1.37±0.05）min，较人工测量使用时间（22.57±2.52）min缩短（

＜0.001）；对于掌倾角，AI测量使用时间为（1.33±0.14）min，较人工测量使用时间（23.70±2.80）min缩短（

＜0.001）。人工测量和AI测量的平均时间增益率分别为93.93%、94.39%。

结论

构建了适配临床场景的桡骨远端掌倾角与尺偏角的自动测量模型，可较为高效、精准地测量桡骨远端骨折掌倾角与尺偏角，为中医手法整复疗效量化评估及大样本临床科研提供工具支撑。

Abstract

Objective

To construct an automatic measurement model for palmar tilt and radial inclination suitable for traditional Chinese medicine （TCM） clinical scenarios， and to validate its accuracy and efficiency in TCM manipulative reduction settings.

Methods

Data on anteroposterior （AP） and lateral X-rays of distal radius fractures were collected from patients admitted to 18 TCM/ integrated TCM and western medicine hospitals in Jiangsu province between September 1st， 2023， and September 1st， 2024， via the Jiangsu Diagnosis and Treatment Big Data Platform for TCM Dominant Diseases. A medical image segmentation framework based on multi-scale feature fusion and edge-awareness was employed， combined with anatomical knowledge specific to TCM orthopedics， to optimize the feature extraction strategy of an artificial intelligence （AI） model. This framework enabled automatic segmentation of fracture regions and measurement of distal radius palmar tilt and radial inclination. The accuracy of the AI model in measuring radial inclination and volar tilt was validated， and the measurement time and average time gain rate of the AI model were compared to those of manual measurement.

Results

A total of 15

444 AP and lateral X-ray images of distal radius fractures were collected， and were divided into a training set （11

144 images， 5066 AP and 6078 lateral）， a validation set （3700 images， 1840 AP and 1860 lateral）， and an independent test set （600 images， 300 AP and 300 lateral） after preprocessing. In the measurement of 300 AP X-ra

ys in the independent test set for radial inclination， when the degree error between AI measurement and manual measurement was ＜3° and ＜5°， AI measurement accuracy was 83% and 93%， respectively. In 300 lateral X-rays in the test set for palmar tilt， when AI measurements had an error of ＜3° and ＜5° compared to manual measurements， corresponding accuracy rate was 78% and 90%， respectively. For 50 X-ray images， AI measurement time was （1.37±0.05） min for radial inclination while manual measurement time was （22.57±2.52） min （

＜0.001）； in terms of palmar tilt， the AI measurement time was （1.33±0.14） min， shorter than （23.70±2.80） min for manual measurement time （

＜0.001）. Average time gain rates for manual and AI measurements were 93.93% and 94.39% respectively.

Conclusion

An automatic measurement model for palmar tilt and radial inclination in distal radius fractures has been established， enabling more accurate and efficient assessment as well as providing a tool to support the quantitative evaluation of the efficacy of TCM manipulative reduction and large-sample clinical research.

关键词

Keywords

references

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