Modelling and artificial intelligence using sensor data to personalise rehabilitation following joint replacement

使用传感器数据进行建模和人工智能，以实现关节置换后的个性化康复

• 批准号: 10024892
• 金额: $ 6.36万
• 依托单位: DYNAMIC METRICS LIMITED
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10024892
• 关键词: Modelling; artificial; intelligence; using; sensor

In 2017, over 218,000 people in the UK had a hip (THR) or knee (TKR) replacement. Provided there are no other underlying conditions, these patients should return to normal activities. However, published studies show that fewer than 50% joint replacement patients regain a normal healthy walk (gait) 1 year post-op. Gait deficiencies have been linked to osteoarthritis in other joints, poor mobility and reduced independence in Activities of Daily Living (ADLs) resulting a lower Quality of Life (QoL). This project will use a medical device plus automated exercises (vGym) to help improve the rehabilitation phase following joint replacement, with the goal to correct gait abnormalities and hence improve a patient's mobility and QoL and reduce healthcare costs.GaitSmart plus vGym, an innovative cloud based, smart sensor system, will be used to determine hip and knee replacement patients' gait kinematics in the outpatient clinic and provide exercises. In this project this will be linked to an artificial intelligence (AI) machine learning system, to optimise the personalised rehabilitation programme.Trials will take place on unilateral hip and knee replacement patients from Norwich Hospital. Patients in the intervention group will receive personalised exercise programmes at each appointment; 6, 9, 12 and 15 weeks post-op, based on their GaitSmart data. A control group will follow the Standard of Care (SoC).Evidence of clinical efficacy will be determined by comparing digital gait kinematics data, speed, PROMS and QoL data from patients following the new care pathway and SoC at the start and end of the intervention period. The economic benefit to the NHS will be determined by comparing all patient outcome data for both groups and predicting future costs based on gait deficiencies relative to changes in QoL.For this project, DML will develop artificial intelligence (AI) so the personalised exercise programmes are produced automatically. The GaitSmart test and automated exercise programme will be an integral part of the patient's rehabilitation and delivered by Healthcare Assistants.GaitSmart has already completed a GaitSmart intervention study in to the NHS for older patients who have fallen and are under the care of a community hospital. These patients received four GS sessions and an automated GaitSmart personalised exercise programme and the clinical and health economic data presented to MPs. This approach is clinically effective and produces a positive ROI. The learnings will be applied to this study.

2017年，英国有超过21.8万人接受了髋关节(THR)或膝关节(TKR)置换。如果没有其他潜在情况，这些患者应该恢复正常活动。然而，已发表的研究表明，只有不到50%的关节置换患者在术后1年恢复正常的健康行走(步态)。步态缺陷与其他关节的骨关节炎、行动不便和日常生活活动(ADL)独立性降低有关，从而导致生活质量(QOL)降低。该项目将使用医疗设备加自动练习(VGym)来帮助改善关节置换后的康复阶段，目标是纠正步态异常，从而提高患者的机动性和生活质量，并降低医疗成本。GaitSmart plus vGym是一种基于云的创新智能传感器系统，将用于在门诊确定髋关节和膝关节置换患者的步态运动学并提供锻炼。在这个项目中，这将连接到人工智能(AI)机器学习系统，以优化个性化康复计划。试验将在诺维奇医院的单侧髋关节和膝关节置换患者身上进行。干预组的患者将在每次预约时，根据他们的GaitSmart数据，在术后6、9、12和15周接受个性化的锻炼计划。对照组将遵循护理标准(SoC)。临床疗效的证据将通过比较数字步态运动学数据、速度、Proms和QOL数据来确定，这些数据来自遵循新护理路径的患者和干预期间开始和结束时的SoC。NHS的经济效益将通过比较两组患者的所有结果数据，并根据步态缺陷与质量变化相关的预测未来成本来确定。在这个项目中，DML将开发人工智能(AI)，以便自动生成个性化的锻炼计划。GaitSmart测试和自动锻炼计划将是患者康复过程中不可或缺的一部分，并由医疗助理提供。GaitSmart已经完成了一项针对跌倒并由社区医院护理的老年患者的GaitSmart干预研究，该研究已提交给NHS。这些患者接受了四次GS会议和一个自动化的GaitSmart个性化锻炼计划，以及提交给MPS的临床和健康经济数据。这种方法在临床上是有效的，并且产生了正的ROI。所学知识将应用于本研究。