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.

项目概要/摘要 人们普遍认为，前十字韧带术后无法恢复受伤前的生物力学 重建（ACL-R）手术是创伤后高患病率的关键因素之一 骨关节炎（PTOA）。精准康复，是指向正确的人传递正确的反馈 鉴于可穿戴设备的最新进展，现在已成为预防 PTOA 的可行方法 传感和计算机视觉技术。灵活且不显眼的皮肤斑块可以客观地量化 走出诊所并提供实时触觉反馈，而来自智能手机的简单视频可以 评估物理治疗质量并提供纠正性视觉或听觉反馈。为了有效地应用 用于预防 PTOA 的新兴智能健康技术，产生的多模式和多变量数据 必须对这些传感器采集的数据进行蒸馏，以识别可通过生物反馈靶向的 PTOA 数字生物标志物 未来的治疗。因此，这项拟议工作的中心目标是确定特征是否 从视频和可穿戴传感器（数字生物标记）中提取的步态可以预测纵向变化 从定量磁共振成像 (qMRI) 中提取的软骨微观结构（早期 PTOA）。我们的 中心假设是，可以在手术后的头几个月内通过以下方法预测未来 PTOA 的风险： 从可穿戴传感器和视频被动收集数据。这个假设得到了我们之前的工作的支持 关于关节炎前的受试者，我们证明可穿戴传感数据可以预测有害的变化 表明 OA 风险的软骨微观结构。为完成本次工作的总体目标， 物理治疗、自然环境行走和软骨健康将得到纵向监测。 将使用计算机视觉量化术前和术后物理治疗期间的运动正确性 和机器学习算法。将在基线（3 周）、第 3 周和第 9 周监测实验室外运动 手术后几个月，表皮传感器放置在大腿和小腿上。定量 MRI 数据将 在基线（3 周）、手术后 3 个月和 18 个月收集。具体来说，我们将确定（1）步态是否 通过可穿戴传感器测量的对称性恢复可以预测术后 18 个月内的 qMRI 变化 (2) 物理治疗质量（在可通过被动计算机视觉算法量化的范围内）是否可以 预测术后 9 个月内步态对称性的恢复。这项工作之所以具有创新性，是因为它突破了 目前在实验室研究生物力学在 PTOA 中的作用的规范。相反，我们将使用 可穿戴传感、计算机视觉和机器学习，以生成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、技术- 辅助康复——临床护理的范式转变。此外，新的 PTOA 生物标志物的发现 可以提高新手术技术的临床试验效率，同时所提出的框架也 可扩展到原发性 OA 以及可能的其他骨科疾病的研究和预防。