| 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 |
|
摘要点击次数: 259
全文下载次数: 67
投稿时间：2024-07-02
|
| 作者 | Author | 单位 | Address | E-Mail |
| 张明 |
ZHANG Ming |
解放军总医院第四医学中心骨科医学部, 北京 100048
解放军医学院, 北京 100853 |
Department of Orthopaedics, the Fourth Medical Center of Chinese PLA General Hospital, Beijing 100048, China
Chinese PLA Medical College, Beijing 100853, China |
|
| 眭亚楠 |
SUI Ya-nan |
清华大学, 北京 100084 |
Tsinghua University, Beijing 100084, China |
ysui@tsinghua.edu.cn |
| 王铖 |
WANG Cheng |
解放军总医院第四医学中心骨科医学部, 北京 100048 |
Department of Orthopaedics, the Fourth Medical Center of Chinese PLA General Hospital, Beijing 100048, China |
|
| 张浩冲 |
ZHANG Hao-chong |
解放军总医院第四医学中心骨科医学部, 北京 100048 |
Department of Orthopaedics, the Fourth Medical Center of Chinese PLA General Hospital, Beijing 100048, China |
|
| 蔡志威 |
CAI Zhi-wei |
解放军总医院第四医学中心骨科医学部, 北京 100048 |
Department of Orthopaedics, the Fourth Medical Center of Chinese PLA General Hospital, Beijing 100048, China |
|
| 张权磊 |
ZHANG Quan-lei |
解放军医学院, 北京 100853 |
Chinese PLA Medical College, Beijing 100853, China |
|
| 张宇 |
ZHANG Yu |
滨州市人民医院, 山东 滨州 256600 |
Binzhou People's hospital, Binzhou 256600, Shandong, China |
|
| 夏天天 |
XIA Tian-tian |
中国人民解放军军事科学院军事医学研究院, 北京 100039 |
Academy of Military Medical Sciences PLA Academy of Military Sciences, Beijing 100039, China |
|
| 祖潇然 |
ZU Xiao-ran |
解放军医学院, 北京 100853 |
Chinese PLA Medical College, Beijing 100853, China |
|
| 黄一健 |
HUANG Yi-jian |
解放军医学院, 北京 100853 |
Chinese PLA Medical College, Beijing 100853, China |
|
| 黄从书 |
HUANG Cong-shu |
中国人民解放军军事科学院军事医学研究院, 北京 100039 |
Academy of Military Medical Sciences PLA Academy of Military Sciences, Beijing 100039, China |
|
| 李想 |
LI Xiang |
解放军总医院第四医学中心骨科医学部, 北京 100048 |
Department of Orthopaedics, the Fourth Medical Center of Chinese PLA General Hospital, Beijing 100048, China |
stevelee301@163.com |
|
| 期刊信息：《中国骨伤》2024年，第37卷，第9期，第855-861页 |
| DOI：10.12200/j.issn.1003-0034.20240321 |
| 基金项目:国家自然科学基金(编号:U22A2052) |
|
中文摘要:
目的: 探讨全膝关节置换术(total knee arthroplasty,TKA)术后的早期步态特征及临床结果。
方法: 自2023年2月到2023年7月采用TKA治疗单侧膝骨关节炎(knee osteoarthritis,KOA)患者26例,男4例,女22例;年龄57~85(67.58±6.49)岁;身体质量指数(body mass index,BMI)为18.83~38.28(26.43±4.15) kg·m-2;左膝14例,右膝12例;Kellgren-Lawrence分级,Ⅲ级6例,Ⅳ级20例;病程1~14(5.54±3.29)年。使用智能手机分别于术前、术后6周拍摄患者起立行走、行走侧拍、蹲起、仰卧屈膝的影像视频,通过人体姿势估计框架OpenPose分析步频、步长、步长时间、步速、膝关节主动屈膝角度、步幅、双下肢支撑相时间以及蹲姿中最大屈髋、屈膝角度。分别于术前及术后6周采用Western Ontario and McMaster大学骨关节炎指数(Western Ontario and McMaster Universities Osteiarthritis Index, WOMAC)评分和美国膝关节协会(Knee Society score,KSS)进行临床疗效评价。
结果: 所有患者获得随访,时间5~7(6.00±0.57)周。WOMAC总分由术前的(64.85±11.54)分,减少至术后6周的(45.81±7.91)分(P＜0.001);KSS由术前(101.19±9.58)分,提高至术后6周的(125.50±10.32)分(P＜0.001)。患侧步速、步频、步幅分别由术前的(0.32±0.10) m·s-1、(96.35±24.18)步·分-1、(0.72±0.14) m,提高至术后的6周的(0.48±0.11) m·s-1、(104.20±22.53)步·分-1、(0.79±0.10) m(P＜0.05)。双下肢支撑时间和主动屈膝角度由术前的(0.31±0.38) s、(125.21±11.64)°,减少至术后6周的(0.11±0.04) s、(120.01±13.35)°(P＜0.05)。术前可以完成蹲起动作的11例,术后6周可以完成的13例,术前和术后6周同时可以完成的9例。9例蹲姿最大屈膝角度由术前的76.29°~124.11°提高至术后6周的91.35°~134.12°,最大屈髋角度由术前的103.70°~147.25°提高至术后6周的118.61°~149.48°。
结论: 基于人工智能影像识别步态分析技术是一种安全、有效的方法可以定量识别出患者步态的变化。KOA患者在行TKA后膝关节疼痛缓解,功能得以改善,TKA术后患肢的支撑能力有所改善,患者的步频、步幅、步速得到了提升,双下肢整体运动节律更为协调。 |
| 【关键词】全膝关节置换术 人工智能 膝骨关节炎 |
| |
| Early gait analysis after total knee arthroplasty based on artificial intelligence dynamic image recognition |
|
ABSTRACT
Objective To explore early postoperative gait characteristics and clinical outcomes after total knee arthroplasty (TKA).
Methods From February 2023 to July 2023,26 patients with unilateral knee osteoarthritis (KOA) were treated with TKA,including 4 males and 22 females,aged from 57 to 85 years old with an average of (67.58±6.49) years old;body mass index (BMI) ranged from 18.83 to 38.28 kg·m-2 with an average of (26.43±4.15) kg·m-2;14 patients on the left side,12 patients on the right side;according to Kellgren-Lawrence(K-L) classification,6 patients with grade Ⅲ and 20 patients with grade IV;the courses of disease ranged from 1 to 14 years with an average of (5.54±3.29) years. Images and videos of standing up and walking,walking side shot,squatting and supine kneeling were taken with smart phones before operation and 6 weeks after operation. The human posture estimation framework OpenPose were used to analyze stride frequency,step length,step length,step speed,active knee knee bending angle,stride length,double support phase time,as well as maximum hip flexion angle and maximum knee bending angle on squatting position. Western Ontario and McMaster Universities (WOMAC) arthritis index and Knee Society Score (KSS) were used to evaluate clinical efficacy of knee joint.
Results All patients were followed up for 5 to 7 weeks with an average of (6.00±0.57) weeks. The total score of WOMAC decreased from (64.85±11.54) before operation to (45.81±7.91) at 6 weeks after operation (P＜0.001). The total KSS was increased from (101.19±9.58) before operation to (125.50±10.32) at 6 weeks after operation (P＜0.001). The gait speed,stride frequency and stride length of the affected side before operation were (0.32±0.10) m·s-1,(96.35±24.18) steps·min-1,(0.72±0.14) m,respectively;and increased to (0.48±0.11) m·s-1,(104.20±22.53) steps·min-1,(0.79±0.10) m at 6 weeks after operation (P＜0.05). The lower limb support time and active knee bending angle decreased from (0.31±0.38) s and (125.21±11.64) ° before operation to (0.11±0.04) s and (120.01±13.35) ° at 6 weeks after operation (P＜0.05). Eleven patients could able to complete squat before operation,13 patients could able to complete at 6 weeks after operation,and 9 patients could able to complete both before operation and 6 weeks after operation. In 9 patients,the maximum bending angle of crouching position was increased from 76.29° to 124.11° before operation to 91.35° to 134.12° at 6 weeks after operation,and the maximum bending angle of hip was increased from 103.70° to 147.25° before operation to 118.61° to 149.48° at 6 weeks after operation.
Conclusion Gait analysis technology based on artificial intelligence image recognition is a safe and effective method to quantitatively identify the changes of patients' gait. Knee pain of KOA was relieved and the function was improved,the supporting ability of the affected limb was improved after TKA,and the patient's stride frequency,stride length and stride speed were improved,and the overall movement rhythm of both lower limbs are more coordinated. |
| KEY WORDS Total knee arthroplasty Artificial intelligence Knee osteoarthriti |
| |
| 引用本文，请按以下格式著录参考文献： |
| 中文格式: | 张明,眭亚楠,王铖,张浩冲,蔡志威,张权磊,张宇,夏天天,祖潇然,黄一健,黄从书,李想.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[J].中国骨伤,2024,37(9):855~861 |
| 英文格式: | ZHANG Ming,SUI Ya-nan,WANG Cheng,ZHANG Hao-chong,CAI Zhi-wei,ZHANG Quan-lei,ZHANG Yu,XIA Tian-tian,ZU Xiao-ran,HUANG Yi-jian,HUANG Cong-shu,LI Xiang.Early gait analysis after total knee arthroplasty based on artificial intelligence dynamic image recognition[J].zhongguo gu shang / China J Orthop Trauma ,2024,37(9):855~861 |
|
| 阅读全文 下载 查看/发表评论 下载PDF阅读器 |
| 关闭 |
|
|
|
