Alejandro García-Rudolph, Mark Andrew Wright, Claudia Teixidó-Font, Rocio Sanchez-Carrion, Gunnar Cedersund, Eloy Opisso
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引用次数: 0
Abstract
Background: Digital Twins (DTs) have transitioned from theory to reality, with growing applications in healthcare. Data generated by technologies (e.g. rehabilitation robots), essential for DT implementation, though widely produced in clinical settings, remains untapped in DT stroke rehabilitation, highlighting a gap compared to broader healthcare use.
Objectives: We conducted a scoping review to i) define DT rehabilitation objectives, their input data, generation methods and user involvement; ii) analyze mechanisms underpinning DT models and outputs; iii) map key stakeholders driving innovation; iv) identify desirable properties for DT studies from broader healthcare literature and map them to stroke rehabilitation DT studies.
Methods: Following PRISMA-ScR guidelines, PubMed, Scopus, Web of Science and Google Scholar were searched for studies including only empirical data. Full-text reviews were conducted by three reviewers through repeated calibration.
Results: Sixteen studies were included, addressing five rehabilitation objectives: upper-limb (10), gait (3), and engagement, mental health, and general/planning (1 each). Patient sample sizes varied widely, with one retrospective study including 1,216 patients, while 15 studies involved 54 patients in total (median = 1).We identified 16 DTs mechanisms (e.g. variational autoencoders, Hill muscle models) and outcomes (e.g. exoskeleton control, upper-limb exercise delivery, gait torque estimation, impaired hand-mobility quantification). Academic institutions conducted 12 studies, Europe contributed 8 studies across 6 countries. Of 25 desirable properties identified, 8 (e.g. reproducible algorithms) showed high adoption, while 15 (e.g. cost-effectiveness, clinical integration) showed low/very low adoption by included studies.
Conclusions: DTs in stroke rehabilitation show promise, though challenges remain (e.g. patient involvement, scalability).
背景:数字孪生(DTs)已经从理论转变为现实,在医疗保健领域的应用越来越多。对DT疗法实施至关重要的技术(如康复机器人)所产生的数据,虽然在临床环境中广泛产生,但在DT疗法中风康复中仍未得到开发,与更广泛的医疗保健应用相比,突出了差距。目标:我们进行了范围审查,以i)定义DT康复目标,其输入数据,生成方法和用户参与;ii)分析支撑DT模型和输出的机制;Iii)绘制驱动创新的关键利益相关者地图;iv)从更广泛的医疗文献中确定DT研究的理想特性,并将其映射到卒中康复DT研究中。方法:按照PRISMA-ScR指南,检索PubMed、Scopus、Web of Science和谷歌Scholar,检索仅包含经验数据的研究。全文评审由三位审稿人通过反复校正进行。结果:纳入16项研究,涉及5个康复目标:上肢(10)、步态(3)、参与、心理健康和总体/计划(各1)。患者样本量差异很大,一项回顾性研究包括1216名患者,而15项研究共涉及54名患者(中位数= 1)。我们确定了16种DTs机制(如变分自编码器、Hill肌肉模型)和结果(如外骨骼控制、上肢运动交付、步态扭矩估计、手部活动障碍量化)。学术机构进行了12项研究,欧洲贡献了6个国家的8项研究。在确定的25个理想特性中,8个(例如可重复算法)显示高采用率,而15个(例如成本效益,临床整合)显示低采用率/非常低采用率。结论:DTs在脑卒中康复中的应用前景光明,但仍存在挑战(如患者参与、可扩展性)。
期刊介绍:
Topics in Stroke Rehabilitation is the leading journal devoted to the study and dissemination of interdisciplinary, evidence-based, clinical information related to stroke rehabilitation. The journal’s scope covers physical medicine and rehabilitation, neurology, neurorehabilitation, neural engineering and therapeutics, neuropsychology and cognition, optimization of the rehabilitation system, robotics and biomechanics, pain management, nursing, physical therapy, cardiopulmonary fitness, mobility, occupational therapy, speech pathology and communication. There is a particular focus on stroke recovery, improving rehabilitation outcomes, quality of life, activities of daily living, motor control, family and care givers, and community issues.
The journal reviews and reports clinical practices, clinical trials, state-of-the-art concepts, and new developments in stroke research and patient care. Both primary research papers, reviews of existing literature, and invited editorials, are included. Sharply-focused, single-issue topics, and the latest in clinical research, provide in-depth knowledge.