Zhao Keyang, Mei Yihan, Wang Xiaoling, Ma Weihui, Shen Wei
Zhongshan Hospital, Fudan University, Shanghai, China.
School of Computer Science, East China Normal University, Shanghai, China.
BMC Musculoskelet Disord. 2025 Jul 16;26(1):687. doi: 10.1186/s12891-025-08921-4.
Satisfied reduction of fracture is hard to achieve. The purpose of this study is to develop a virtual fracture reduction technique using conditional GAN (Generative Adversarial Network), and evaluate its performance in simulating and guiding reduction of femoral neck fracture, which is hard to reduce. We compared its reduction quality with manual reduction performed by orthopedic surgeons. It is a pilot study for augmented reality assisted femoral neck fracture surgery.
To establish the gold standard of reduction, we invited an orthopedic surgeon to perform virtual reduction registration with reference to the healthy proximal femur. The invited orthopedic surgeon also performed manual reduction by Mimics software to represent the capability of human doctor. Then we trained conditional GAN models on our dataset, which consisted 208 images from 208 different patients. For displaced femoral neck fractures, it is not easy to measure the accurate angles, like Pauwels angle, of the fracture line. However, the fracture lines would be clearer after reduction. We compared the results of manual reduction, conditional GAN models and registration by Pauwels angle, Garden index and satisfied reduction rate. We tried different number of downsampling (α) to optimize the performance of conditional GAN models.
There were 208 pre-surgical CT scans from 208 patients included in our study (age 69.755 ± 13.728, including 88 men). The Pauwles angles of conditional GAN model(α = 0) was 38.519°, which was significantly more stable than manual reduction (44.647°, p < 0.001). The Garden indices of conditional GAN model(α = 0) was 176.726°, which was also significantly more stable than manual reduction (163.590°, p = 0.002). The satisfied reduction rate of conditional GAN model(α = 0) was 88.372%, significantly higher than manual reduction (53.488%, p < 0.001). The Pauwels angles, Garden indices and satisfied reduction rate of conditional GAN model(α = 0) showed no difference to registration.
Conditional GAN model(α = 0) can achieve better performance in the virtual reduction of femoral neck fracture than orthopedic surgeon.
骨折难以实现满意复位。本研究旨在开发一种使用条件生成对抗网络（Conditional Generative Adversarial Network，Conditional GAN）的虚拟骨折复位技术，并评估其在模拟和指导难以复位的股骨颈骨折复位中的性能。我们将其复位质量与骨科医生进行的手法复位进行比较。这是一项关于增强现实辅助股骨颈骨折手术的初步研究。
为建立复位的金标准，我们邀请一名骨科医生参照健康的股骨近端进行虚拟复位配准。受邀的骨科医生还通过Mimics软件进行手法复位以体现人类医生的能力。然后我们在包含208名不同患者的208张图像的数据集上训练条件GAN模型。对于移位的股骨颈骨折，测量骨折线的准确角度（如 Pauwels 角）并不容易。然而，复位后骨折线会更清晰。我们通过 Pauwels角、Garden指数和满意复位率比较了手法复位、条件GAN模型和配准的结果。我们尝试不同数量的下采样（α）以优化条件GAN模型的性能。
我们的研究纳入了208例患者的208份术前CT扫描（年龄69.755±13.728，包括88名男性）。条件GAN模型（α = 0）的Pauwles角为38.519°，显著比手法复位更稳定（44.647°，p < 0.001）。条件GAN模型（α = 0）的Garden指数为176.726°，也显著比手法复位更稳定（163.590°，p = 0.002）。条件GAN模型（α = 0）的满意复位率为88.372%，显著高于手法复位（53.488%，p < 0.001）。条件GAN模型（α = 0）的Pauwles角、Garden指数和满意复位率与配准无差异。
条件GAN模型（α = 0）在股骨颈骨折的虚拟复位中比骨科医生表现更好。
