Department of Orthopaedic, The First People's Hospital of Fuyang, Hangzhou, China 311400.
Comput Math Methods Med. 2022 May 26;2022:7811200. doi: 10.1155/2022/7811200. eCollection 2022.
To develop a deep learning-assisted recovery and nursing system after total hip arthroplasty and to conduct clinical trials in order to verify its accuracy.
In our study, based on manual labeling, the human hip X-ray image library was established, and the deep neural network based on Mask R-CNN was built. The labeled medical images were used to train the model, providing reference for nursing decision after hip replacement. A total of 80 patients with hip injury from 2016 to 2019 were selected for the study. In our paper, the patients were divided into experimental group and control group. The pertinence and effectiveness of the model for postoperative care were evaluated by comparing the hip pain (VAS index), recovery (Harris score), self-care ability (Barthel index), and postoperative complication rate between the two groups.
The pain and complications in the experimental group were significantly lower than those in the control group, the difference being statistically significant ( < 0.05); the recovery of hip joint and self-care ability were higher than those in the control group, the difference being statistically significant ( < 0.05); the other differences were not statistically significant ( > 0.05).
The application of deep learning method in the rapid nursing after total hip replacement can significantly improve the nursing ability. Compared with the traditional method, it has stronger pertinence, faster postoperative recovery, lower incidence of complications, and greatly improves the postoperative quality of life of patients with hip injury.
开发一种深度学习辅助全髋关节置换术后康复和护理系统，并进行临床试验以验证其准确性。
在我们的研究中，基于手动标记，建立了人类髋关节 X 射线图像库，并构建了基于 Mask R-CNN 的深度神经网络。使用标记的医学图像来训练模型，为髋关节置换后的护理决策提供参考。共有 80 名 2016 年至 2019 年髋关节损伤的患者被纳入本研究。在我们的论文中，患者被分为实验组和对照组。通过比较两组的髋关节疼痛（VAS 指数）、恢复（Harris 评分）、自理能力（Barthel 指数）和术后并发症发生率，评估模型对术后护理的针对性和有效性。
实验组的疼痛和并发症明显低于对照组，差异具有统计学意义（<0.05）；髋关节恢复和自理能力均高于对照组，差异具有统计学意义（<0.05）；其他差异无统计学意义（>0.05）。
深度学习方法在全髋关节置换术后快速护理中的应用，可显著提高护理能力。与传统方法相比，它具有更强的针对性、更快的术后恢复、更低的并发症发生率，极大地提高了髋关节损伤患者的术后生活质量。
