Application and development trends of artificial intelligence in rehabilitation after hip and knee arthroplasty_West China Medical Journal

Authors：

WANG Yiqin ¹ , LIAO Qian ¹ , DONG Feng ¹ , ZHU Min ² ,  HU Rong ¹

1. Department of Orthopedics, Mianyang Central Hospital, Mianyang, Sichuan 621000, P. R. China;
2. Department of Pediatrics, Mianyang Anzhou District People’s Hospital, Mianyang, Sichuan 621000, P. R. China;

Corresponding author：

HU Rong, Email: tcmhurong@163.com

Keywords：

Artificial intelligence; hip arthroplasty; knee arthroplasty; rehabilitation medicine; smart nursing; wearable devices

DOI：

10.7507/1002-0179.202504231

Video：

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Abstract Full text Figures/Tables Video References Cited by

With the rapid advancement of artificial intelligence (AI), its application in the rehabilitation of patients undergoing hip and knee arthroplasty has been increasingly emphasized. AI has the potential to enhance the precision and individualization of rehabilitation training, improve patient adherence, and optimize overall outcomes. This review summarizes the current progress of AI in postoperative rehabilitation following hip and knee arthroplasty, focusing on its roles in rehabilitation assessment, intelligent training, and remote rehabilitation. Furthermore, the advantages of AI in improving efficiency, accuracy, and patient engagement are highlighted, while existing challenges, including insufficient clinical evidence, high technological costs, and ethical concerns, are critically discussed. Finally, potential future directions, such as the integration of AI with virtual reality and wearable devices, are proposed. This review aims to provide valuable insights for clinical practice and future research in the rehabilitation of hip and knee arthroplasty.

1.	Karasavvidis T, Pagan Moldenhauer CA, Haddad FS, et al. Current concepts in alignment in total knee arthroplasty. J Arthroplasty, 2023, 38(7 Suppl 2): S29-S37.
2.	Konnyu KJ, Thoma LM, Cao W, et al. Rehabilitation for total knee arthroplasty: a systematic review. Am J Phys Med Rehabil, 2023, 102(1): 19-33.
3.	Omar I, Kunutsor SK, Bertram W, et al. Rehabilitation for revision total knee replacement: survey of current service provision and systematic review. BMC Musculoskelet Disord, 2023, 24(1): 91.
4.	Sloan M, Premkumar A, Sheth NP. Projected volume of primary total joint arthroplasty in the U. S. , 2014 to 2030. J Bone Joint Surg Am, 2018, 100(17): 1455-1460.
5.	Wei Z, Xu Y, Feng B, et al. The impact of COVID-19 on hip and knee arthroplasty surgical volume in China. Int Orthop, 2024, 48(1): 49-56.
6.	李川, 钱东阳, 王斌, 等. 中国髋关节置换入路选择临床实践指南(2021 年版). 中华关节外科杂志(电子版), 2021, 15(6): 651-659.
7.	许嘉乐, 傅利勤, 吴红, 等. 康复自我效能在髋/膝关节置换术后住院患者领悟社会支持与康复锻炼依从性间的中介作用. 上海交通大学学报(医学版), 2024, 44(8): 959-967.
8.	黄寅殊, 单浩杰, 于晓巍. 人工智能赋能的全髋关节置换术进展. 医用生物力学, 2024, 39(6): 1228-1234.
9.	贾震宇, 沈洪园, 郑坤, 等. 人工智能辅助术前规划的多杯联合垫块个性化髋臼重建技术在复杂初次全髋关节置换术及翻修术中的临床应用. 中华骨与关节外科杂志, 2025, 18(1): 36-42.
10.	张明, 眭亚楠, 王铖, 等. 基于人工智能动态影像识别的全膝关节置换术后早期步态分析研究. 中国骨伤, 2024, 37(9): 855-861.
11.	丁旭壮. 术前三维规划软件(AI-HIP)在初次全髋关节置换术中的价值: 一项回顾性研究. 石家庄: 河北医科大学, 2022.
12.	闵美鹏, 吴进, Rafi U, 等. 全髋关节置换前人工智能(AI)规划的作用与意义. 中国组织工程研究, 2024, 28(9): 1372-1377.
13.	宋平. 人工智能辅助全膝关节置换术前规划系统的研发、应用与评价. 北京: 中国人民解放军医学院, 2022.
14.	唐佩福. 智慧骨科生态体系的构建与趋势. 中华骨科杂志, 2020, 40(23): 1567-1573.
15.	冯雨果, 刘宇, 周晋. 可穿戴惯性传感器在全膝关节置换术后步态分析中的应用进展. 皮革科学与工程, 2023, 33(6): 52-58.
16.	Daskivich TJ, Houman J, Lopez M, et al. Association of wearable activity monitors with assessment of daily ambulation and length of stay among patients undergoing major surgery. JAMA Netw Open, 2019, 2(2): e187673.
17.	郑文丽, 杨玉妹, 狄建忠. 关节置换围手术期感染影响因素分析. 中华骨与关节外科杂志, 2019, 12(3): 231-235.
18.	Crocker A, Kornilo A, Conly J, et al. Using administrative data to determine rates of surgical site infections following spinal fusion and laminectomy procedures. Am J Infect Control, 2021, 49(6): 759-763.
19.	Wu G, Cheligeer C, Southern DA, et al. Development of machine learning models for the detection of surgical site infections following total hip and knee arthroplasty: a multicenter cohort study. Antimicrob Resist Infect Control, 2023, 12(1): 88.
20.	Hwang D, Ahn S, Park YB, et al. Deep learning-based muscle segmentation and quantification of full-leg plain radiograph for sarcopenia screening in patients undergoing total knee arthroplasty. J Clin Med, 2022, 11(13): 3612.
21.	梁清钊, 董广元, 李楠雁, 等. 人工智能在肌少症精准预测与智能管理中的应用进展. 军事护理, 2025, 42(1): 96-99.
22.	张飞, 李毛毛, 李铭麟, 等. 人工智能在肌少症中的应用研究进展. 实用老年医学, 2024, 38(1): 3-6, 10.
23.	袁博. 基于平衡障碍康复机器人全髋关节置换术后患者康复处方的研究. 遵义: 遵义医科大学, 2023.
24.	Ajami S, Teimouri F. Features and application of wearable biosensors in medical care. J Res Med Sci, 2015, 20(12): 1208-1215.
25.	Setoguchi D, Kinoshita K, Kamada S, et al. Hybrid assistive limb improves restricted hip extension after total hip arthroplasty. Assist Technol, 2022, 34(1): 112-120.
26.	Fang T, Cao W, Chen C, et al. A soft exosuit for hip extension assistance of the elderly. Technol Health Care, 2021, 29(4): 837-841.
27.	马宇杰. 下肢智能负重康复机器人在人工全膝关节置换术后早期康复中的应用. 延安: 延安大学, 2023.
28.	陈颂, 左君君, 马民. 康复机器人联合早期路径式训练对全膝关节置换患者术后康复影响. 社区医学杂志, 2023, 21(4): 212-216, 220.
29.	Torrealba RR, Udelman SB, Fonseca-Rojas ED. Design of variable impedance actuator for knee joint of a portable human gait rehabilitation exoskeleton. Mech Mach Theory, 2017, 116: 248-261.
30.	蔡立柏, 刘延锦, 刘阳阳, 等. 下肢康复机器人的应用对全膝关节置换术患者康复的影响. 护理学杂志, 2022, 37(5): 5-9.
31.	Ramkumar PN, Haeberle HS, Ramanathan D, et al. Remote patient monitoring using mobile health for total knee arthroplasty: validation of a wearable and machine learning-based surveillance platform. J Arthroplasty, 2019, 34(10): 2253-2259.
32.	王佳冰, 孟庆芳, 冯慧慧, 等. 基于人工智能的骨科护理用下肢训练康复系统设计. 自动化与仪器仪表, 2023(5): 186-190.
33.	Chen J, He F, Wu Q, et al. Identifying self-reported health-related problems in home-based rehabilitation of older patients after hip replacement in China: a machine learning study based on Omaha system theory. BMC Med Inform Decis Mak, 2023, 23(1): 268.
34.	Ulivi M, Orlandini L, Meroni V, et al. Remote management of patients after total joint arthroplasty via a web-based registry during the COVID-19 pandemic. Healthcare (Basel), 2021, 9(10): 1296.
35.	Kuether J, Moore A, Kahan J, et al. Telerehabilitation for total hip and knee arthroplasty patients: a pilot series with high patient satisfaction. HSS J, 2019, 15(3): 221-225.
36.	Prvu Bettger J, Green CL, Holmes DN, et al. Effects of virtual exercise rehabilitation in-home therapy compared with traditional care after total knee arthroplasty: VERITAS, a randomized controlled trial. J Bone Joint Surg Am, 2020, 102(2): 101-109.
37.	孔宁, 赵奕威, 杨佩, 等. 数字智能化在关节外科的应用. 中国骨与关节杂志, 2024, 13(11): 865-869.
38.	周谋望. 我国骨科康复的研究热点及发展趋势. 中国临床保健杂志, 2021, 24(2): 160-161.

1. Karasavvidis T, Pagan Moldenhauer CA, Haddad FS, et al. Current concepts in alignment in total knee arthroplasty. J Arthroplasty, 2023, 38(7 Suppl 2): S29-S37.
2. Konnyu KJ, Thoma LM, Cao W, et al. Rehabilitation for total knee arthroplasty: a systematic review. Am J Phys Med Rehabil, 2023, 102(1): 19-33.
3. Omar I, Kunutsor SK, Bertram W, et al. Rehabilitation for revision total knee replacement: survey of current service provision and systematic review. BMC Musculoskelet Disord, 2023, 24(1): 91.
4. Sloan M, Premkumar A, Sheth NP. Projected volume of primary total joint arthroplasty in the U. S. , 2014 to 2030. J Bone Joint Surg Am, 2018, 100(17): 1455-1460.
5. Wei Z, Xu Y, Feng B, et al. The impact of COVID-19 on hip and knee arthroplasty surgical volume in China. Int Orthop, 2024, 48(1): 49-56.
6. 李川, 钱东阳, 王斌, 等. 中国髋关节置换入路选择临床实践指南(2021 年版). 中华关节外科杂志(电子版), 2021, 15(6): 651-659.
7. 许嘉乐, 傅利勤, 吴红, 等. 康复自我效能在髋/膝关节置换术后住院患者领悟社会支持与康复锻炼依从性间的中介作用. 上海交通大学学报(医学版), 2024, 44(8): 959-967.
8. 黄寅殊, 单浩杰, 于晓巍. 人工智能赋能的全髋关节置换术进展. 医用生物力学, 2024, 39(6): 1228-1234.
9. 贾震宇, 沈洪园, 郑坤, 等. 人工智能辅助术前规划的多杯联合垫块个性化髋臼重建技术在复杂初次全髋关节置换术及翻修术中的临床应用. 中华骨与关节外科杂志, 2025, 18(1): 36-42.
10. 张明, 眭亚楠, 王铖, 等. 基于人工智能动态影像识别的全膝关节置换术后早期步态分析研究. 中国骨伤, 2024, 37(9): 855-861.
11. 丁旭壮. 术前三维规划软件(AI-HIP)在初次全髋关节置换术中的价值: 一项回顾性研究. 石家庄: 河北医科大学, 2022.
12. 闵美鹏, 吴进, Rafi U, 等. 全髋关节置换前人工智能(AI)规划的作用与意义. 中国组织工程研究, 2024, 28(9): 1372-1377.
13. 宋平. 人工智能辅助全膝关节置换术前规划系统的研发、应用与评价. 北京: 中国人民解放军医学院, 2022.
14. 唐佩福. 智慧骨科生态体系的构建与趋势. 中华骨科杂志, 2020, 40(23): 1567-1573.
15. 冯雨果, 刘宇, 周晋. 可穿戴惯性传感器在全膝关节置换术后步态分析中的应用进展. 皮革科学与工程, 2023, 33(6): 52-58.
16. Daskivich TJ, Houman J, Lopez M, et al. Association of wearable activity monitors with assessment of daily ambulation and length of stay among patients undergoing major surgery. JAMA Netw Open, 2019, 2(2): e187673.
17. 郑文丽, 杨玉妹, 狄建忠. 关节置换围手术期感染影响因素分析. 中华骨与关节外科杂志, 2019, 12(3): 231-235.
18. Crocker A, Kornilo A, Conly J, et al. Using administrative data to determine rates of surgical site infections following spinal fusion and laminectomy procedures. Am J Infect Control, 2021, 49(6): 759-763.
19. Wu G, Cheligeer C, Southern DA, et al. Development of machine learning models for the detection of surgical site infections following total hip and knee arthroplasty: a multicenter cohort study. Antimicrob Resist Infect Control, 2023, 12(1): 88.
20. Hwang D, Ahn S, Park YB, et al. Deep learning-based muscle segmentation and quantification of full-leg plain radiograph for sarcopenia screening in patients undergoing total knee arthroplasty. J Clin Med, 2022, 11(13): 3612.
21. 梁清钊, 董广元, 李楠雁, 等. 人工智能在肌少症精准预测与智能管理中的应用进展. 军事护理, 2025, 42(1): 96-99.
22. 张飞, 李毛毛, 李铭麟, 等. 人工智能在肌少症中的应用研究进展. 实用老年医学, 2024, 38(1): 3-6, 10.
23. 袁博. 基于平衡障碍康复机器人全髋关节置换术后患者康复处方的研究. 遵义: 遵义医科大学, 2023.
24. Ajami S, Teimouri F. Features and application of wearable biosensors in medical care. J Res Med Sci, 2015, 20(12): 1208-1215.
25. Setoguchi D, Kinoshita K, Kamada S, et al. Hybrid assistive limb improves restricted hip extension after total hip arthroplasty. Assist Technol, 2022, 34(1): 112-120.
26. Fang T, Cao W, Chen C, et al. A soft exosuit for hip extension assistance of the elderly. Technol Health Care, 2021, 29(4): 837-841.
27. 马宇杰. 下肢智能负重康复机器人在人工全膝关节置换术后早期康复中的应用. 延安: 延安大学, 2023.
28. 陈颂, 左君君, 马民. 康复机器人联合早期路径式训练对全膝关节置换患者术后康复影响. 社区医学杂志, 2023, 21(4): 212-216, 220.
29. Torrealba RR, Udelman SB, Fonseca-Rojas ED. Design of variable impedance actuator for knee joint of a portable human gait rehabilitation exoskeleton. Mech Mach Theory, 2017, 116: 248-261.
30. 蔡立柏, 刘延锦, 刘阳阳, 等. 下肢康复机器人的应用对全膝关节置换术患者康复的影响. 护理学杂志, 2022, 37(5): 5-9.
31. Ramkumar PN, Haeberle HS, Ramanathan D, et al. Remote patient monitoring using mobile health for total knee arthroplasty: validation of a wearable and machine learning-based surveillance platform. J Arthroplasty, 2019, 34(10): 2253-2259.
32. 王佳冰, 孟庆芳, 冯慧慧, 等. 基于人工智能的骨科护理用下肢训练康复系统设计. 自动化与仪器仪表, 2023(5): 186-190.
33. Chen J, He F, Wu Q, et al. Identifying self-reported health-related problems in home-based rehabilitation of older patients after hip replacement in China: a machine learning study based on Omaha system theory. BMC Med Inform Decis Mak, 2023, 23(1): 268.
34. Ulivi M, Orlandini L, Meroni V, et al. Remote management of patients after total joint arthroplasty via a web-based registry during the COVID-19 pandemic. Healthcare (Basel), 2021, 9(10): 1296.
35. Kuether J, Moore A, Kahan J, et al. Telerehabilitation for total hip and knee arthroplasty patients: a pilot series with high patient satisfaction. HSS J, 2019, 15(3): 221-225.
36. Prvu Bettger J, Green CL, Holmes DN, et al. Effects of virtual exercise rehabilitation in-home therapy compared with traditional care after total knee arthroplasty: VERITAS, a randomized controlled trial. J Bone Joint Surg Am, 2020, 102(2): 101-109.
37. 孔宁, 赵奕威, 杨佩, 等. 数字智能化在关节外科的应用. 中国骨与关节杂志, 2024, 13(11): 865-869.
38. 周谋望. 我国骨科康复的研究热点及发展趋势. 中国临床保健杂志, 2021, 24(2): 160-161.

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