Digital Biomarkers of Post-traumatic Osteoarthritis: Toward Precision Rehabilitation

创伤后骨关节炎的数字生物标志物：迈向精准康复

• 批准号: 10357435
• 负责人: Eni Halilaj
• 金额: $ 57.07万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10357435
• 关键词: Age; Algorithms; American; Anterior Cruciate Ligament; Arthritis; Biofeedback Training; Biological Markers; Biomechanics; Body mass index; Caring; Cartilage; Cellular Phone; Characteristics; Clinic; Clinical Trials; Computer Vision Systems; Data; Degenerative polyarthritis; Development; Devices; Early treatment; Environment; Exercise; Exhibits; Failure; Feedback; Future; Gait; Goals; Health; Health Technology; High Prevalence; Home; Injury; Joints; Knowledge; Laboratories; Laboratory Research; Longevity; Machine Learning; Magnetic Resonance Imaging; Measures; Modernization; Monitor; Movement; Operative Surgical Procedures; Orthopedics; Patient Rights; Patients; Performance; Physical therapy; Postoperative Period; Prevention; Primary Prevention; Radiology Specialty; Reconstructive Surgical Procedures; Recovery; Rehabilitation therapy; Research; Risk; Risk Factors; Role; Rupture; SMART health; Sports; System; Techniques; Technology; Thigh structure; Time; Travel; Vertebral column; Vision; Wearable Computer; Work; anterior cruciate ligament injury; anterior cruciate ligament reconstruction; auditory feedback; base; clinical care; clinical translation; computerized tools; digital; flexibility; gait examination; gait rehabilitation; gait symmetry; haptic feedback; haptics; improved; individualized medicine; innovation; light weight; machine learning algorithm; machine vision; multimodality; personalized intervention; portability; post-operative rehabilitation; rehabilitation strategy; restoration; sensor; sex; skin patch; visual feedback; wearable device; wearable sensor technology

PROJECT SUMMARY/ABSTRACT It is widely accepted that failure to restore pre-injury biomechanics after anterior cruciate ligament reconstruction (ACL-R) surgery is one of the key contributing factors to the high prevalence of post-traumatic osteoarthritis (PTOA). Precision rehabilitation, which refers to the delivery of the right feedback to the right patient at the right time, is now a feasible approach for PTOA prevention given recent advances in wearable sensing and computer vision technologies. Flexible and unobtrusive skin patches can objectively quantify movement out of the clinic and deliver real-time haptic feedback, while simple videos from smartphones can assess physical therapy quality and deliver corrective visual or auditory feedback. To effectively apply emerging smart-health technologies toward PTOA prevention, the multi-modal and multivariate data produced by these sensors must be distilled to identify digital biomarkers of PTOA that can be targeted with biofeedback therapy in the future. Accordingly, the central objective of this proposed work is to determine if characteristics of gait extracted from video and wearable sensors (digital biomarkers) can predict longitudinal changes in cartilage microstructure (early PTOA) extracted from quantitative Magnetic Resonance Imaging (qMRI). Our central hypothesis is that future risk of PTOA can be predicted in the first few months after surgery using passively collected data from wearable sensors and video. This hypothesis is supported by our previous work on pre-arthritic subjects, where we demonstrated that wearable sensing data could predict detrimental changes in cartilage microstructure that are indicative of OA risk. To accomplish the overall objective of this work, physical therapy, natural environment ambulation, and cartilage health will be monitored longitudinally. Exercise correctness during pre- and post-operative physical therapy will be quantified using computer vision and machine learning algorithms. Out-of-lab movement will be monitored at baseline (3 weeks), 3, and 9 months after surgery with epidermal sensors placed on the thighs and shanks. Quantitative MRI data will be collected at baseline (3 weeks), 3 and 18 months after the surgery. Specifically, we will determine (1) if gait symmetry restoration measured by wearable sensors can predict qMRI changes up to 18 months post-surgery and (2) if physical therapy quality, to the extent that is quantifiable with passive computer vision algorithms, can predict gait symmetry restoration up to 9 months post-surgery. This work is innovative because it breaks with the current norms of studying the role of biomechanics in PTOA in the laboratory. Instead, we will use wearable sensing, computer vision, and machine learning to generate previously unavailable knowledge on the role of natural environment biomechanics. If successful, this work could enable personalized, technology- assisted rehabilitation—a paradigm shift in clinical care. Additionally, the discovery of new PTOA biomarkers could improve the efficiency of clinical trials for new surgical techniques, while the proposed framework is also extensible to the study and prevention of primary OA, and possibly other orthopaedic conditions.

项目摘要/摘要 人们普遍认为，前交叉韧带后未能恢复受伤前的生物力学 重建（ACL-R）手术是引起创伤后高流行率的关键因素之一 骨关节炎（PTOA）。精确康复，指的是向右提供正确的反馈 鉴于最新的可穿戴状况进展 传感和计算机视觉技术。柔性和不引人注目的皮肤贴剂可以客观地量化 从诊所移出并提供实时触觉反馈，而智能手机的简单视频可以 评估物理治疗质量并提供正确的视觉或听觉反馈。有效应用 新兴的智能医疗技术用于PTOA预防，生产的多模式和多元数据 通过这些传感器必须进行蒸馏，以识别可以用生物反馈靶向的PTOA的数字生物标志物 将来的治疗。根据这项提议的工作的各个方面，是确定是否特征 从视频和可穿戴传感器（数字生物标志物）中提取的搅拌器可以预测纵向变化 从定量磁共振成像（QMRI）提取的软骨微结构（早期PTOA）。我们的 中心假设是，可以在手术后的头几个月预测PTOA的未来风险 从可穿戴传感器和视频中被动收集的数据。我们以前的工作支持了这一假设 在临床前的受试者上，我们证明可穿戴感应数据可以预测有害的变化 在软骨微观结构中，表示OA风险。为了实现这项工作的总体目标， 物理疗法，自然环境和软骨健康将纵向监测。 在术前和术后物理疗法期间的运动正确性将使用计算机视觉进行量化 和机器学习算法。在基线（3周），3和9 手术后的几个月，带有表皮传感器放在大腿和小腿上。定量MRI数据将是 手术后的基线（3周），3和18个月收集。具体来说，我们将确定（1）是否步态 可穿戴传感器测量的对称恢复可以预测QMRI在手术后长达18个月更改 （2）如果物理疗法质量，则可以通过被动计算机视觉算法进行量化的质量 预测手术后9个月长达9个月的步态对称性恢复。这项工作是创新的，因为它与 研究生物力学在PTOA中在实验室中的作用的当前规范。相反，我们将使用 可穿戴感应，计算机视觉和机器学习，以产生以前无法获得的知识 自然环境生物力学的作用。如果成功，这项工作可以实现个性化的技术 - 辅助康复 - 临床护理的范式转变。此外，发现新的PTOA生物标志物 可以提高新手术技术的临床试验效率，而拟议的框架也是 可扩展对原发性OA的研究和预防，以及可能的其他骨科条件。