无标记三维运动捕捉在ACL损伤高水平运动员重返运动决策中的应用：叙述性综述及临床实施路径建议
简介
这篇叙事综述探讨无标记3D运动捕捉技术在ACL损伤高水平运动员重返运动决策中的应用价值，指出传统RTS标准难以识别导致移植物失效和对侧损伤的残余神经肌肉缺陷，而该技术可量化运动质量以弥补此不足。作者基于2005至2025年文献，提出了一套结合系列生物力学测试、参数特异性过关阈值和纵向监测的临床实施路径，并指出当前证据在动态多平面动作及预测再伤风险方面尚不成熟…
英文摘要
BACKGROUND AND OBJECTIVE: Anterior cruciate ligament (ACL) injury remains a career-defining event for high-level athletes, and current return to sport (RTS) criteria often fail to detect residual neuromuscular deficits that drive graft failure and contralateral tears. Three-dimensional (3D) markerless motion capture has emerged as a scalable technology that may address these gaps by quantifying movement quality during sport-specific tasks. The objective of this narrative review is to summarize the current evidence on markerless 3D motion capture in ACL rehabilitation and RTS decision-making, with a specific focus on clinical implementation pathways for high-level athletes. METHODS: A comprehensive literature search was conducted across PubMed/MEDLINE, Embase, and the Cochrane Library from January 2005 through May 2025 using terms related to markerless motion capture, biomechanical analysis, ACL reconstruction, and RTS. English-language original research, systematic reviews, validation studies, and clinical commentaries were considered. Reference lists of included articles were hand-searched to identify additional relevant publications. KEY CONTENT AND FINDINGS: Traditional biomechanical tools, including dual fluoroscopy, marker-based motion capture, and inertial measurement units, have advanced understanding of ACL injury mechanisms but face practical barriers limiting routine clinical use. Markerless systems use computer vision and machine learning to generate joint kinematics and, when integrated with force plates or pressure insoles, kinetics during sport-specific tasks. Platforms such as OpenCap expand access for research and large-scale field testing, whereas clinical systems such as Theia3D demonstrate the strongest current evidence for clinical integration, with validation studies showing acceptable sagittal-plane accuracy but less mature evidence for dynamic, multiplanar RTS movements. A structured RTS pathway incorporating serial biomechanical testing, parameter-specific clearance thresholds, and longitudinal monitoring is proposed. Remaining challenges include limited validation samples, incomplete evidence for sport-specific multiplanar tasks, and sparse prospective data linking markerless-derived metrics with clinically meaningful outcomes. CONCLUSIONS: Markerless motion capture is positioned to become a valuable component of RTS decision-making by providing scalable, objective, and ecologically valid assessment of movement quality. Future work should prioritize standardized capture protocols, multicenter normative datasets stratified by sport and sex, and prospective studies connecting markerless-derived metrics with reinjury and performance outcomes.