Digital twins in stroke rehabilitation: a scoping review of objectives, data sources, mechanisms, outcomes, and desirable properties

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).