Home-based telerehabilitation for pediatric neurological motor disorders: Current trends and future perspectives. A systematic review and meta-analysis

Abstract

Objective

Telerehabilitation is a rapidly growing area within telehealth, providing clinical rehabilitation services through telecommunication technologies to enhance accessibility, improve care quality, and support patient functionality. Pediatric motor rehabilitation has gained increasing recognition for its vital role in promoting neuromotor development, coordination, and independence in children with congenital or acquired motor disabilities. This systematic review analyzed home-based telerehabilitation interventions for pediatric neurological motor disorders, from the neonatal period onward, aiming to outline current trends and future directions.

Methods

A comprehensive search was conducted across Medline, EBSCO, and Web of Science, covering records up to February 2025. Peer-reviewed articles were included without restrictions on study design. The focus was on pediatric and adolescent populations with neurodevelopmental or neuromuscular disorders undergoing telerehabilitation or remote rehabilitative interventions.

Results

The findings support the potential efficacy of tele-neurorehabilitation in improving motor outcomes, with promising results for children with cerebral palsy and hemiplegia. Interventions were shown to enhance motor function (SMD = 0.48; 95% CI: 0.23 to 0.72), range of motion (SMD = 0.31; 95% CI: 0.11 to 0.51), and walking ability (SMD = 0.25; 95% CI: 0.03 to 0.46), suggesting that remote rehabilitation can be an effective complement or alternative to traditional in-person therapy in pediatric care. Moreover, beyond functional improvements, several studies reported positive effects on children's quality of life, highlighting the broader psychosocial benefits of remote interventions.

Conclusions

These results underscore the holistic value of telerehabilitation, reinforcing its role as a comprehensive and accessible strategy in pediatric rehabilitation services. Multidimensional assessments are essential to capture the broader benefits of telerehabilitation and to guide the development of more holistic, child- and family-centered care models.

Keywords

Telerehabilitation telemedicine pediatrics child adolescent neurologic disorders motor activity home care services

Introduction

The advancement and widespread availability of Information and Communication Technologies (ICTs) have created new opportunities for delivering healthcare services remotely, both during hospitalization and after discharge.¹

According to the World Health Organization (WHO), telehealth refers to the provision of healthcare services when patients and providers are separated by distance, using ICTs to support communication for diagnosis, treatment, research, evaluation, and continuing professional development.² Originally adopted to bridge geographic barriers and improve access in underserved areas telehealth has since evolved into a critical component of modern healthcare systems.^3,4 Its relevance surged dramatically during the COVID-19 pandemic, when social distancing and overwhelmed healthcare facilities forced a shift toward virtual care solutions.

Telerehabilitation is an essential and expanding area within the broader field of telehealth.^5–8 It represents a clinical rehabilitation service that leverages telehealth technologies to enhance accessibility, improve care quality, and maximize patient functionality.⁹

In recent years, telerehabilitation has attracted growing attention as an innovative strategy for managing musculoskeletal conditions and motor impairments and has been proposed as an effective care model also for pediatric populations.^10–13 The WHO has acknowledged the effectiveness of telerehabilitation as a viable service delivery model for rehabilitation professionals.^2,14 Similarly, the American Occupational Therapy Association and the American Physical Therapy Association support the use of telerehabilitation in clinical settings, recognizing its potential to improve patient outcomes and its flexibility in addressing the specific needs of patients and their families.^15,16

Despite its recognized potential, the use of remote rehabilitation services in pediatric care remained limited before the COVID-19 pandemic. This was primarily due to cultural factors, limited access to technology, and challenges related to regulatory frameworks and reimbursement policies.^17,18

More recently, childhood motor rehabilitation has gained increasing recognition as a critical component in supporting neuromotor development, enhancing coordination, and fostering independence in children with congenital or acquired motor disabilities. Within this framework, telerehabilitation has emerged as a flexible and effective modality for delivering individualized therapeutic interventions without requiring physical presence. Its advantages include the enhancement of both gross and fine motor skills,¹⁹ improved self-perception of physical competence,²⁰ reduced access barriers, remote treatment personalization and monitoring, and greater opportunities for active family involvement in the rehabilitation process. Additionally, telerehabilitation encourages collaboration between therapists and caregivers, who become active participants in the therapeutic process and gain practical tools to support the child's motor development in daily life.²⁰

In this systematic review, we analyzed the use of home-based telerehabilitation for pediatric neurological motor disorders, starting from the neonatal age, with the aim of presenting current trends and future perspectives in this field of telemedicine.

Material and methods

Study design

The review was conducted in accordance with the Cochrane Handbook for Systematic Reviews guidelines, as well as other relevant methodological standards for systematic reviews and meta-analyses.²¹ The reporting of findings followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, reported in Table S1.²² The PRISMA flow diagram summarizing the selection process is presented in Figure 1. The Population-Concept-Context (PCC) framework²³ was used to create search strings, as detailed in Table 1.

Figure 1.

Preferred reporting items for systematic reviews and meta-analyses (PRISMA) flow diagram.

Table 1.

PCC elements.

PCC elements	Definitions
Population	Pediatric subjects from infancy to adolescence, including infants, children, and teenagers.
Concept	Neurodevelopmental and neuromuscular motor disorders, such as cerebral palsy, muscular atrophy, neuropathies, myopathies, hemiplegia, epilepsy.
Context	Home-based interventions delivered remotely, including telerehabilitation, digital therapy, mobile health, and self-managed rehabilitation approaches.

Protocol registration

The protocol for systematic review and meta-analysis was registered in the International Prospective Register of Systematic Reviews (PROSPERO) under registration number CRD420251036180.²⁴

Search strategy

A comprehensive literature search was performed across three major biomedical databases: Medline, Elton B. Stephens Company (EBSCOhost), and Web of Science, encompassing records from database inception to February 2025. Search strategy combined controlled vocabulary (e.g., Medical Subject Headings [MeSH]) and relevant free-text terms, customized according to the thesaurus, and indexing structure of each database. No limitations were applied concerning study design or publication type; eligible sources included peer-reviewed articles. The search targeted the pediatric and adolescent population (including neonates, infants, children, and adolescents) diagnosed with neurodevelopmental or neuromuscular disorders and undergoing telerehabilitation or remotely delivered rehabilitative interventions.

Study selection process

Tailored terms such as “Children,” “Telerehabilitation,” and “Neurodevelopmental disorders” and their synonyms were combined to create search strings (detailed in Table S2). Boolean operators (AND, OR) were used to refine search. Two independent reviewers carried out the study selection process in accordance with the predefined inclusion and exclusion criteria. Initially, duplicate records were removed. Next, the titles and abstracts of all retrieved references were screened to exclude studies that were clearly irrelevant. Full-text articles of potentially eligible studies were then obtained and assessed in detail to determine their suitability for inclusion. Any discrepancies between reviewers were resolved through discussion, and if consensus could not be reached, a third reviewer was consulted to make the final decision. Additionally, the reference lists of all included studies were manually screened to identify any further relevant publications.

Eligibility criteria

Studies were considered eligible for inclusion if they met the following criteria: (i) population comprised pediatric patients (0–18 years), (ii) participants presented with neurological motor disorders or motor impairments, including but not limited to neurodevelopmental disorders, cerebral palsy, muscular atrophy, neuropathies, myopathies, hemiplegia, or other neuromuscular conditions; (iii) the intervention involved home-based telerehabilitation or remote rehabilitation modalities, such as digital therapy, mobile health and telemedicine; and (iv) articles were published in English. Moreover, studies were excluded if they met any of the following criteria: (i) involved adult populations aged more than 18 years; (ii) addressed non-neurological or non-motor disorders; (iii) focused exclusively on in-person rehabilitation without any component of remote or digital intervention; (iv) were structured as observational studies, qualitative studies and reviews.

Data selection and extraction

To streamline the study selection process, all references retrieved from the database searches were imported into Rayyan^®, an online platform tailored for systematic review management.²⁵ Duplicate entries were carefully removed through a combination of automated tools and manual verification. Following this, two reviewers (PP and LG) independently conducted a two-stage screening process. The initial phase involved evaluating titles and abstracts to eliminate studies that were clearly ineligible. Articles deemed potentially relevant then proceeded to a full-text review, conducted independently and without influence from the other reviewer's decisions. Any disagreements that arose during the selection process were discussed to reach a mutually agreed-upon resolution. In cases where consensus could not be achieved, a third reviewer (VR) was brought in to provide an objective decision. This approach was designed to uphold consistency and reduce bias throughout the selection process.

Each article that advanced to full-text screening was reviewed in-depth to assess its methodological soundness and relevance to the research question. Only those studies that met the inclusion criteria and demonstrated sufficient methodological quality were included in the final synthesis. For every study included, a standardized set of information was extracted, including: author(s), year of publication, study purpose, design, sample size, participants’ age, setting and intervention and main outcomes. The extracted information is reported in Table 2.

Table 2.

Descriptive characteristics of the studies included in the systematic review.

Authors, year of publication	Study purpose	Design	Sample size (M; F)	Age	Setting	Intervention	Main outcome
Saussez et al. (2023)	Evaluate integrating semi-immersive VR with Hand–Arm Bimanual Intensive Training Including Lower Extremity (HABIT-ILE) in children with unilateral CP	Randomized controlled trial	40 (20 M; 20F)	5–18 years	Rehab center and home	HABIT-ILE intervention in a high-dosage day-camp setting on 10–12 consecutive weekdays, to a total of 90 h.	Results showed improved upper limb (UL) function and motor planning
Piovesana et al. (2016)	Assess web-based multimodal intervention (Mitii) for executive function in unilateral CP	Randomized controlled trial	101 (51 M; 50F)	8–18 years	Home-based	Mitii (Move It to Improve It)—web-based therapy combining motor and cognitive tasks, 30 min/day, 5 days/week, for 20 weeks	Improved working memory and executive functioning
James et al. (2015)	Evaluate Mitii's effect on motor/executive function in bilateral CP	Randomized controlled trial	102 (51 M; 51F)	8–18 years	Home-based	Mitii program, 30 min/day, 6 days/week, for 20 weeks	Improvement in motor skills and executive functioning
Chen et al. (2016)	Compare home vs lab-based robotic ankle training	Pilot randomized comparative trial	41 (31 M; 10F)	7–18 years	Home & lab	Robotic ankle training at home using a portable device, 3 sessions/week, 20–30 min per session, over 6 weeks	Both improved, home more engaging
Preston et al. (2015)	Evaluate home computer-assisted rehab for UL function	Pilot randomized controlled trial	15 (9 M; 6F)	5–12 years	Home-based	Home-based computer-assisted UL therapy, 10–30 min/day, 5 days/week, for 5 weeks	No UL function improvement
Wang et al. (2021)	Assess feasibility of commercial exergames for home CP therapy	Randomized pilot trial	18 (7 M; 11F)	5–12 years	Home-based	Nintendo Wii Fit, 120 min/session, 2 times/week, for 4 weeks	Improved motivation, some gains in balance and strength
Farr et al. (2019)	Evaluate VR therapy impact on UL function in CP	Randomized controlled trial	15 (6 M; 9F)	5–16 years	Home-based	Nintendo Wii Fit^TM; 30 min/session, 3 sessions/week, over 12 weeks	Improved motor outcomes and satisfaction
Celikel et al. (2023)	Effect of telerehabilitation on QoL in CP during COVID-19	Randomized controlled trial	25 (9 M; 16F)	6–17 years	Home-based	Motor learning-based treatment was applied 2 days a week, 40 min a day for 8 weeks.	QoL significantly improved
Choi et al. (2023)	Assess VR-enhanced UL training in children with brain injury	Randomized controlled trial	35 (18 M; 17F)	4–17 years	Clinic and home-based	VR-enhanced program with wearable multi-inertial measurement unit (IMU) sensors for at least 30 min/day, 5 days/week, for 6 weeks.	Positive motor planning and function trends
Szturm et al. (2022)	Game-based dual-task program for motor/cognitive performance	Randomized controlled trial	20 (14 M; 6F)	4–9 years	Clinic and home	Dual Task computer game-based therapy. 12 weeks at a frequency of 3 therapy sessions per week. Each session was 45 min long.	Coordination, balance, and attention improved
Sgandurra et al. (2017)	Test CareToy System for early motor/visual development	Randomized controlled trial	41 (19 M; 22F)	3–9 months	Home-based	CareToyIntervention, training is programmed daily for 30–45 min for 4 weeks including weekends (total of 28 days) .	Enhanced motor and visual development
Errante et al. (2024)	Evaluate model-based action observation therapy in hemiplegia	Randomized controlled trial	26 (12 M; 14F)	6–16 years	Home-based	Action observation treatment (AOT)—The duration of daily sessions was about 1 h per day. The frequency of the intervention was 3 days per week, for the first 2 weeks, and 2 days for the last week (as a whole, 8 h of treatment).	Improved UL motor performance
MacIntosh et al. (2020)	Explore biofeedback video game therapy for youth with CP	Randomized single-case	19 (9 M; 10F)	8–18 years	Home-based	Bootle Blast exergaming at home, 30 min/session, recommended 3–6 sessions/week for 4 weeks.	Better adherence, motivation, and engagement
Panzeri et al. (2022)	Effectiveness of VR gait rehab in pediatric ABI	Pilot study	6 (4 M; 2F)	7–18 years	Clinic and home	Gait Real-time Analysis Interactive Lab (GRAIL) intervention—20 sessions, each lasting 45 min.	Motor coordination improved
Chan-Víquez et al. (2023)	Usability of home videogame intervention for hemiplegia	Mixed methods	4 (3 M; 1F)	8–13 years	Home-based	Bootle Blast with Microsoft Kinetec at home for 15–20 min, 3/4 sessions/ week for 4–12 weeks.	High engagement and usability
Coley et al. (2022)	Feasibility of PedBotHome robotic ankle device	Pilot study	8 (1 M; 7F)	9–16 years	Home-based	PedBotHome robotic ankle device used with game interface, 10 min, 3 set of exercise, 24 days.	Feasible, parents satisfied
Yu et al. (2023)	Pilot personalized motor/respiratory telerehab for NMDs	Pilot study	8 (N/A)	8–18 years	Home-based	Personalized telerehabilitation program (instructional videos), 3 sessions/ week, each 15–30 min, for 16 weeks.	Improved adherence, function, HRQoL
Macchitella et al. (2024)	Two-step XR + telerehab for CP	Pilot study	7 (5 M; 2F)	4–10 years	Clinic and home	K-VRRS-based treatment (3D virtual games) included two telerehabilitation sessions per day, six days a week, with each session lasting approximately 45 min.	Good usability and adherence
Gerber et al. (2016)	Feasibility of exergame system for pediatric UL rehab	Feasibility study	15 (8 M; 7F)	5–18 years	Home-based	Home-based motion-controlled exergames (YouGrabber^® system), at least 5 times for 30 min for 2 weeks.	High engagement, useful feedback
Burdea et al. (2011)	Long-term PlayStation telerehab for hand function in CP	Descriptive case series	3 (N/A)	N/A	Home-based	PlayStation-based telerehabilitation, sessions varied 30–45 min, 3 times/ week, for 6 months; included hand tracking and real-time feedback.	Improved hand coordination
Weightman et al. (2011)	Feasibility of home computer therapy for UL rehab	Pilot study	18 (13 M; 5F)	5–16 years	Home-based	Home-based computer-assisted training for at least 30/minutes/session for 4 weeks	Good feasibility, mild function gains
Golomb et al. (2010)	In-home VR videogame rehab for CP	Proof-of-concept pilot	3 (2 M; 1F)	13–15 years	Home-based	In-home virtual reality videogame (5DT Ultra Glovea and a PlayStation3 game console) rehab with motion sensors, 30–45 min/session, 3–5 sessions/week, for 8 weeks	Improved motor activity
Chen et al. (2015)	VR-based home motor rehab intervention	Case series feasibility	3 (0 M; 3F)	7–10 years	Home-based	VR-based motor therapy (Playstation2 game console – EyeToyPlay VR system), 5 sessions/ week, 30 min/ session, for 8 weeks	High engagement and motor gains
Li et al. (2009)	Develop/test VR UL rehab system for kids	Usability/feasibility study	5 (4 M; 1F)	6–10 years	Home-based	Sony PlayStation 2 with an “EyeToy” video camera, 25–30 min/day, for 10 days	Feasible, potential motor benefit
Huber et al. (2010)	Gaming tech for home-based UL training	Pilot study	3 (2 M; 1F)	13–15 years	Home-based	PlayStation3motor training at home, over two hours/day, 5 days/week, for at least 6 months	Functional gains, satisfaction
Sandlund et al. (2011)	Motion-interactive games for physical activity	Mixed-methods	14 (8 M; 6F)	6–16 years	Home-based	EyeToy (PlayStation2)—Time spent on playing every day was recorded by marking one of the time sequences: 20–30 min; 30–60 min; 60–90 min; 90–120 min; more than 120 min; total 4 weeks.	Moderate activity, positive feedback
Kassee et al. (2017)	Feasibility of Wii training at home for CP	Randomized pilot study	6 (6 M; 0F)	7–12 years	Home-based	Wii Sport, for at least 40 min each day, 5 days a week for 6 weeks	Increased fun, mild UL improvements
Chan-Víquez et al. (2024)	Feasibility and impact of videogaming therapy for CP	Randomized single-case	15 (10 M; 5F)	7–17 years	Home-based	Bootle Blast gaming—mean active playtime across the 8 weeks was 377 min, while mean total time spent engaging with Bootle Blast was 728 min.	Potential UL function enhancement

Note: UL, upper limb; QoL, quality of life; ABI, acquired brain injury; NMDs, neuromuscular disorders; CP, cerebral palsy; M, males; F, females; VR, virtual reality.

Outcome measures

In this systematic review, we examined a range of outcome measures used to assess the efficacy of tele-neurorehabilitation in the pediatric population with motor-related conditions. Across the included studies, key outcomes evaluated included gross motor function, range of movement, general strength, walking function and quality of life. These measures provided a comprehensive overview of the potential benefits of remote rehabilitation interventions for children with motor impairments. Quality of life was considered as an important outcome in the review, although it was not consistently reported across studies. For the purposes of the meta-analysis, only outcome measures that were consistently reported and suitable for quantitative synthesis were included. Specifically, data related to motor function and/or performance, range of movement, and general and or specific strength were analyzed. These outcomes enabled a pooled analysis to assess the overall efficacy of tele-neurorehabilitation in improving motor performance. Although quality of life was included in the systematic review, it could not be incorporated into the meta-analysis due to limited data availability, as only two studies reported on this outcome.

Risk of bias and quality assessments

The methodological quality of the included studies was assessed using validated tools tailored to the specific study designs. For randomized controlled trials (RCTs), the Revised Cochrane Risk-of-Bias Tool (ROB2) was applied. This tool evaluates five domains: the randomization process, deviations from intended interventions, missing outcome data, measurement of outcomes, and selection of the reported results. Two reviewers (VR and AG) independently assessed each domain, rating studies as having “low,” “some concerns,” or “high” risk of bias. Discrepancies were resolved through discussion, with the involvement of a third reviewer (PP) when necessary.

For non-randomized studies, the Risk Of Bias In Non-randomized Studies of Interventions (ROBINS-I) tool was used. This framework addresses seven domains of potential bias: confounding, selection of participants, classification of interventions, deviations from intended interventions, missing data, measurement of outcomes, and selection of reported results. As with the RCTs, two reviewers independently conducted the assessments (LG and VR), resolving any differences by consensus or consultation with a third reviewer (AG).

Importantly, studies were not excluded based solely on their quality assessment. Instead, the results of these appraisals were used to guide the interpretation, weighting, and contextualization of the evidence during synthesis.

Statistical analysis

The effectiveness of telerehabilitation interventions was assessed using a post-pre analysis approach. Standardized mean differences (SMDs) were calculated based on the change from baseline to post-intervention within each study. To ensure comparability across studies, effect sizes were standardized using the pooled baseline standard deviation. A random-effects meta-analysis model was employed, with restricted maximum likelihood (REML) estimation used to account for between-study variance. Each study's contribution to the pooled estimate was weighted using the inverse variance method. Heterogeneity across studies was assessed using the I² statistic and Cochran's Q test, with I² values interpreted according to established guidelines (e.g., 0–40%: low; 30–60%: moderate; 50–90%: substantial; 75–100%: considerable). To assess possible sources of heterogeneity, mixed-effects meta-regression models were used to explore the impact of study-level moderators, including intervention type, age group, and, where applicable, intervention duration. The proportion of heterogeneity explained by moderators was reported using R² values, and the overall contribution of moderators was assessed with the QM statistic. Residual heterogeneity was examined to determine whether the included variables accounted for variation across studies. In addition, prediction intervals were calculated using the method described by Riley, providing an estimate of the likely range of effects in future studies. All analyses were conducted using R (version 4.4) with the “metafor” package.

Results

As shown in Table 2, most of the participants included in the studies were diagnosed with cerebral palsy or hemiplegia, reflecting the primary populations targeted by pediatric motor tele-rehabilitation programs. Furthermore, most interventions were specifically focused on upper limb rehabilitation, which may limit the generalizability of the findings to other motor domains, such as lower limb function or global mobility.

The structured process for identifying and selecting studies is presented in the PRISMA flowchart (Figure 1). After removing duplicate entries, a total of 1008 titles and abstracts were screened. From these, 94 full-text articles were evaluated in more detail. Studies were excluded at this stage for several reasons: (i) the intervention was not performed using any type of technology in the hospital or clinics; (ii) the intervention was performed at home without using the support of any technology. Finally, 28 studies met all inclusion criteria and were included into this systematic review. However, only 21 studies were found eligible to be included in the meta-analysis.

Risk-of-bias assessment for every study is shown in Figure 2. Overall, the risk of bias has risen “some concerns,” with only several pilot studies considered as “high.” Most of the concerns were related to problems in the allocation concealments and in the statistical analyses performed.

Figure 2.

Summary plot of the risk of bias for the included studies, assessed using the Risk Of Bias In Non-randomized Studies of Interventions (ROBINS-I; on the left) and the Risk of Bias tool 2 (RoB 2; on the right).

Regarding overall motor function, the meta-analysis revealed a moderate and statistically significant improvement (SMD = 0.48; 95% CI: 0.23 to 0.72), Figure 3. However, heterogeneity was substantial (I² = 74.2%, P < .001), likely due to the use of heterogeneous assessment tools across studies. Commonly used instruments included the Gross Motor Function Measure (GMFM-66 and GMFM-88), Bruininks–Oseretsky Test of Motor Proficiency, Second Edition (BOT-2), and other functional motor assessments. These tools differ in sensitivity and applicability, which may have contributed to the variability in the estimated and observed effects. In line with the substantial heterogeneity detected, the prediction for motor function had an estimate of 0.48 (95% CI: −0.25 to 1.29), indicating a moderate treatment effect in future studies with a moderate range variability. To examine sources of heterogeneity, a mixed-effects meta-regression was conducted (k = 11) with intervention type as a moderator. The model explained most of the variance (R² = 95.93%, I² = 9.07%, QM (4) = 25.65, P < .001). The estimated effect for computer-based interventions in children and adolescents was significant (SMD = 0.48; 95% CI: 0.29 to 0.67). Compared to this group, the CareToy system showed significantly larger effects (β = 0.85, P < .001), while no significant differences were found for Nintendo Wii-based, VR-based, or PlayStation 2-based interventions.

Figure 3.

Forest plot of standardized mean differences within groups (post–pre estimates) for motor function. A negative value indicates a reduction in outcome after the intervention, whereas a positive value indicates an improvement in outcome after the intervention. Abbreviations: CI, confidence interval; SMD, standardized mean difference.

In the evaluation of muscular strength, a pooled SMD of 0.67 (95% CI: −0.05 to 1.39) was found, indicating a moderate effect size, Figure 4. However, the analysis revealed a not statistically significant effect of telerehabilitation programs. A high degree of heterogeneity was detected (I² = 81.8%, P < .001), likely reflecting both the diversity of strength assessment methods and differences in the specific muscle groups targeted across studies. In line with the substantial heterogeneity detected, the prediction for muscular strength had an estimate of 0.67 (95% CI: −0.93 to 2.27), indicating a wide range of possible treatment effects in future studies. For muscular strength, a mixed-effects meta-regression was conducted (k = 5) with intervention type as a moderator. The model was significant (QM (2) = 10.44, P = .0054), explaining a substantial proportion of between-study variance (R² = 87.33%, I² = 40.80%). The estimated effect size for the reference group (computer-based interventions) was not statistically significant (SMD = 0.33; 95% CI: −0.11 to 0.78; P = .14). Compared to this group, Nintendo Wii-based interventions showed a significantly larger effect on muscular strength (β = 1.83; 95% CI: 0.72 to 2.93; P = .001), while PlayStation 3-based interventions did not differ significantly (β = 0.30; P = .593).

Figure 4.

Forest plot of standardized mean differences within groups (post–pre estimates) for muscular strength. A negative value indicates a reduction in outcome after the intervention, whereas a positive value indicates an improvement in outcome after the intervention. Abbreviations: CI, confidence interval; SMD, standardized mean difference.

For range of motion (ROM), the meta-analysis showed a small but statistically significant improvement (SMD = 0.31; 95% CI: 0.11 to 0.51), with moderate heterogeneity (I² = 39.8%, P = .09), suggesting a fair level of consistency in the observed effects across studies, Figure 5. In line with the moderate heterogeneity detected, the prediction for ROM had an estimate of 0.31 (95% CI; −0.12 to 0.74), showing that future studies might observe small positive changes in ROM.

Figure 5.

Forest plot of standardized mean differences within groups (post–pre estimates) for range of motion (ROM). A negative value indicates a reduction in outcome after the intervention, whereas a positive value indicates an improvement in outcome after the intervention. Abbreviations: CI, confidence interval; SMD, standardized mean difference.

In terms of walking function, a small but statistically significant effect was observed (SMD = 0.25; 95% CI: 0.03 to 0.46), with low heterogeneity (I² = 17.6%, P = .41), indicating a high degree of consistency among studies, Figure 6. Although walking performance was assessed using various functional tests, including the 6-Minute Walk Test (6MWT), the 1-Minute Walk Test (1MWT), and other similar protocols, this variability in outcome measures did not appear to substantially influence the magnitude or direction of the pooled effect. In line with the low heterogeneity detected, the prediction for muscular strength had an estimate of 0.25 (95% CI; −0.05 to 0.54), showing that future studies might observe small positive changes in walking function.

Figure 6.

Forest plot of standardized mean differences within groups (post–pre estimates) for walking function. A negative value indicates a reduction in outcome after the intervention, whereas a positive value indicates an improvement in outcome after the intervention. Abbreviations: CI, confidence interval; SMD, standardized mean difference.

Discussion

The findings of this systematic review and meta-analysis support the potential efficacy of tele-neurorehabilitation in improving motor outcomes in the pediatric population, with particularly promising, and more often assessed, results observed in children with cerebral palsy and hemiplegia. We found that the interventions analyzed were significantly effective in enhancing motor function, range of motion, and walking ability, suggesting that remote rehabilitation strategies can be a reliable complement and/or an alternative to traditional face-to-face occupational therapy in a pediatric setting. Moreover, beyond functional outcomes, several studies also reported improvements in children's quality of life following tele-neurorehabilitation. Although this outcome could not be included in the meta-analysis due to the limited number of studies reporting comparable data, the available evidence indicates that remote interventions may positively impact broader aspects of well-being. This further supports the holistic and comprehensive value of tele-rehabilitation approaches in pediatric care.

Effects of tele-neurorehabilitation on motor function

Our meta-analysis demonstrated a significant improvement in motor function following tele-neurorehabilitation interventions, with a small-to-moderate effect size. While several studies did not reach statistical significance,^26–28 most showed a trend toward positive change.^29–31 These non-significant findings are likely attributable to small sample sizes, limited statistical power, and variability in intervention protocols. Still, the overall direction of the effect consistently favored telehealth-based approaches. Notably, the observed substantial heterogeneity reflects differences in study design, intervention intensity, outcome measures, and the specific motor domains assessed. This underscores the need for greater methodological consistency in future trials, specifically regarding standardized outcome measures. Improving motor function in children with neurologic conditions is not only clinically relevant, but also critical for long-term functional independence.³² Even modest gains can translate into increased participation in everyday activities, including school, play, and self-care, which are foundational to healthy development.³³ The variability observed in the prediction intervals likely stems from differences in motor function assessment methods, the heterogeneity of intervention designs, and the specific motor domains addressed across studies. Although the average effects point toward moderate improvements, the moderate range of possible outcomes underscores the need for cautious interpretation.

Effects of tele-neurorehabilitation on range of motion

Our meta-analysis revealed a small but statistically significant effect of tele-neurorehabilitation on improving range of motion (ROM) in pediatric populations. Although the effect size was modest, the consistency of direction across studies and the relatively low heterogeneity strengthen confidence in the reliability of these findings. This suggests that remote interventions can effectively target joint mobility, particularly in children with neuromotor impairments who are at increased risk of developing ROM restrictions over time.³⁴ Improved ROM is closely linked to functional outcomes, such as reaching, grasping, and ambulation, and is also essential for preventing contractures and maintaining musculoskeletal health. Nonetheless, the gains observed support the integration of tele-rehabilitation into home programs aimed at preserving and enhancing joint flexibility. Regular guided movement sessions, combined with active caregiver participation, appear to contribute to the maintenance of therapeutic routines outside of clinical settings.³⁵ Future studies should employ standardized and quantitative ROM measures and explore long-term outcomes to better determine the durability and clinical impact of these improvements. The prediction for ROM suggests that future studies may observe small but variable improvements. This range reflects differences in assessment tools and intervention protocols, reinforcing the need for standardized assessment procedures.

Effects of tele-neurorehabilitation on walking function and muscular strength

Our findings suggest that tele-neurorehabilitation may contribute to improved walking function in pediatric populations with neurologic conditions. The meta-analysis revealed a small but statistically significant effect with low heterogeneity, indicating a relatively consistent, though modest, benefit across studies. Improved walking ability is a key functional outcome, often linked to greater independence and participation in daily life activities,^36,37 which reinforces the clinical relevance of even small gains in this domain. In contrast, no statistically significant improvement was observed for muscular strength, despite a moderate effect size and several individual studies reporting favorable trends.^38,39 The wide confidence interval and substantial heterogeneity highlight inconsistencies in intervention design and outcome measurement, as well as the limited sample sizes across studies. These limitations make it difficult to draw firm conclusions about the effectiveness of remote interventions for strength enhancement. Overall, while tele-neurorehabilitation shows promise in promoting functional mobility, further research is needed to clarify its role in improving muscular strength, ideally through larger trials employing standardized, strength-specific assessment tools.

However, these conclusions should be interpreted with caution due to the heterogeneity observed across studies, especially in the domains of motor function and muscular strength. This variability can be attributed to several factors, including the diversity of assessment tools used and the differences both in the intervention protocols and in the targeted motor domains. The variability found also in the prediction estimates likely reflects the diversity of walking function and strength measurement protocols, intervention formats, and specific motor domain targeted across study. While the mean effect suggests a moderate improvement, the range of potential outcomes cautions against overly optimistic generalization.

Clinical implications and future perspectives

Implementing tele-rehabilitation in a pediatric neurorehabilitation can help delivering continuous and individualized care within the child's home environment. This is particularly advantageous for families living in rural or underserved areas, or for those facing logistical and financial barriers to in-person services. Moreover, telerehabilitation helps to achieve a more involving model of care, encouraging both parents and caregivers to be active partners in the therapeutic process.¹ Studies have shown that caregiver engagement not only facilitates the delivery of remote interventions but also improves their effectiveness by strengthening family support and integrating therapy into daily routines.^17,34,38 Encouraging caregiver participation is therefore crucial to maximize the outcomes and sustainability of pediatric telerehabilitation programs.

Nonetheless, the successful implementation of telerehabilitation also depends on addressing critical technological and infrastructural barriers. Disparities in access to high-speed internet, appropriate digital devices, and digital literacy represent significant challenges to the equitable delivery of remote rehabilitation services.⁸ Without careful consideration, these gaps may limit the inclusivity and effectiveness of digital health interventions. To promote broader accessibility, future telerehabilitation programs should prioritize adaptable solutions that work across diverse socioeconomic contexts, including low-bandwidth environments and widely available devices.^40,41 Investing in intuitive, user-friendly platforms is crucial to ease the technological burden on families and enhance engagement. Additionally, initiatives to improve digital literacy and partnerships with community organizations can support equitable adoption on a larger scale.⁴² By proactively addressing these barriers, telerehabilitation can fully realize its potential as a high-quality, accessible, and inclusive tool for pediatric care.

As telerehabilitation continues to expand, it is also important to ensure that rehabilitation professionals receive adequate training to deliver care effectively in virtual environments.⁴³ Telerehabilitation programs require adapted communication strategies, proficiency in virtual assessment tools, and innovative therapeutic approaches adapted to digital platforms.⁴⁴ Furthermore, hybrid care models, which combine in-person and remote sessions, could be another promising strategy. Through both in-person assessments and home-based tele-rehabilitation, hybrid models may help sustain therapeutic engagement over time. Particularly for children requiring long-term, intensive rehabilitation, hybrid models may help sustain therapeutic engagement, enhance continuity of care, and optimize clinical outcomes over time.⁴⁵

Moreover, nowadays, the integration of emerging technologies holds great potential to further enhance pediatric telerehabilitation. Tools such as motion-tracking systems, gamified therapy applications, and AI-driven adaptive feedback mechanisms can increase patient motivation, support individualized care plans, and enable real-time progress monitoring.^40,46 Motion-tracking systems, leveraging accessible hardware like high-definition webcams and smartphones equipped with innovative technologies such as LIDAR, allow for precise tracking of body movements.^47,48 Advanced computer vision frameworks facilitate real-time analysis, enabling clinicians to provide immediate corrective feedback and systematically assess progress. Gamified therapeutic applications, through interactive mini-games that reward correct physical performance, can sustain motivation and transform exercises into engaging activities.^48,49 Meanwhile, AI-driven adaptive feedback mechanisms offer a personalized rehabilitation experience by analyzing motion data in real time, delivering tailored feedback, and dynamically adjusting task difficulty based on patient performance.^50–52

The synergistic application of these technologies promises to create a more dynamic, engaging, and patient-centered telerehabilitation environment, ultimately enhancing the effectiveness of remote interventions and strengthening the therapeutic alliance with families.

Conclusions

This study demonstrates the effectiveness and practicality of telerehabilitation in supporting motor and functional improvements in children with developmental and physical impairments. The positive outcomes observed underscore the value of remote delivery models in maintaining therapeutic engagement and facilitating access to care, particularly for families facing geographic, logistical, or socioeconomic barriers to traditional in-person services. The results indicate that telerehabilitation is not only a feasible alternative but also a clinically meaningful approach that can be integrated into standard pediatric rehabilitation pathways. Furthermore, the findings of our study highlight the potential of telerehabilitation to enhance continuity of care, promote greater family involvement in therapy, and extend the reach of interventions beyond conventional clinical settings. These implications are particularly relevant in the context of ongoing healthcare challenges that demand more flexible and inclusive service delivery models. By demonstrating improvements in key functional outcomes, this study supports the broader adoption of telerehabilitation as a strategy for achieving child- and family-centered care goals. It also provides critical evidence for clinicians, policymakers, and healthcare systems seeking to implement more adaptable, and responsive rehabilitation services for pediatric populations.

Take home message

Telerehabilitation has a strong potential as an effective and accessible intervention for improving gross motor function, targeted upper and lower limb abilities, and walking performance in children with neurological conditions, particularly those with cerebral palsy and hemiplegia. Despite variability among studies, the overall evidence supports its use as a valuable complement or alternative to traditional in-person therapy. Additionally, telerehabilitation promotes active caregiver participation and enhances access to care, especially for families in underserved areas. To maximize its benefits, future research should prioritize the use of standardized outcome measures, address technological and infrastructural barriers, and use emerging technologies to deliver personalized rehabilitation experiences.

Supplemental Material

sj-docx-1-dhj-10.1177_20552076251357504 - Supplemental material for Home-based telerehabilitation for pediatric neurological motor disorders: Current trends and future perspectives. A systematic review and meta-analysis

Supplemental material, sj-docx-1-dhj-10.1177_20552076251357504 for Home-based telerehabilitation for pediatric neurological motor disorders: Current trends and future perspectives. A systematic review and meta-analysis by Valeria Calcaterra, Luca Marin, Luca Guardamagna, Alessandro Gatti, Virginia Rossi, Pamela Patanè, Matteo Vandoni and Gianvincenzo Zuccotti in DIGITAL HEALTH

Supplemental Material

sj-docx-2-dhj-10.1177_20552076251357504 - Supplemental material for Home-based telerehabilitation for pediatric neurological motor disorders: Current trends and future perspectives. A systematic review and meta-analysis

Supplemental material, sj-docx-2-dhj-10.1177_20552076251357504 for Home-based telerehabilitation for pediatric neurological motor disorders: Current trends and future perspectives. A systematic review and meta-analysis by Valeria Calcaterra, Luca Marin, Luca Guardamagna, Alessandro Gatti, Virginia Rossi, Pamela Patanè, Matteo Vandoni and Gianvincenzo Zuccotti in DIGITAL HEALTH

Supplemental Material

sj-docx-3-dhj-10.1177_20552076251357504 - Supplemental material for Home-based telerehabilitation for pediatric neurological motor disorders: Current trends and future perspectives. A systematic review and meta-analysis

Supplemental material, sj-docx-3-dhj-10.1177_20552076251357504 for Home-based telerehabilitation for pediatric neurological motor disorders: Current trends and future perspectives. A systematic review and meta-analysis by Valeria Calcaterra, Luca Marin, Luca Guardamagna, Alessandro Gatti, Virginia Rossi, Pamela Patanè, Matteo Vandoni and Gianvincenzo Zuccotti in DIGITAL HEALTH

Footnotes

ORCID iDs

Valeria Calcaterra

Luca Marin

Luca Guardamagna

Alessandro Gatti

Virginia Rossi

Matteo Vandoni

Gianvincenzo Zuccotti

Author contributions

Conceptualization: V.C., M.V., L.M., G.Z.; independent collection of the contributions: A.G., A.G., V.R., P.P.; writing—original draft preparation: A.G., A.G., V.R., P.P.; writing—review and editing: V.C., M.V., L.M., G.Z.; supervision: V.C., M.V., L.M., G.Z. All authors have read and agreed to the published version of the manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported within the (1) MUSA—Multilayered Urban Sustainability Action—project, funded by the European Union—NextGenerationEU, under the National Recovery and Resilience Plan (NRRP) Mission 4 Component 2 Investment Line 1.5: Strengthening of research structures and creation of R&D “innovation ecosystems,” set up of “territorial leaders in R&D”_CUD G43C22001370007; (2) “PSR_LINEA6_2021/Research Development Plan-University Grand Challenges-Line 6 Disease prevention and sustainability and efficiency of healthcare: a distributed virtual hospital for Italy (e-Care).”

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Supplemental material

Supplemental material for this article is available online.

References

Del Lucchese

Parravicini

Filogna

, et al. The wide world of technological telerehabilitation for pediatric neurologic and neurodevelopmental disorders – a systematic review. Front Public Health 2024; 12: 1295273.

World Health Organization (WHO). WHO-ITU global standard for accessibility of telehealth services [Internet], https://www.who.int/publications/i/item/9789240050464 (2022, cited 11 April 2025).

Anawade

Sharma

Gahane

. A comprehensive review on exploring the impact of telemedicine on healthcare accessibility. Cureus [Internet], https://www.cureus.com/articles/232773-a-comprehensive-review-on-exploring-the-impact-of-telemedicine-on-healthcare-accessibility (2024, cited 11 April 2025).

Alenoghena

Ohize

Adejo

, et al. Telemedicine: a survey of telecommunication technologies, developments, and challenges. JSAN 2023; 12: 20.

Parmanto

Saptono

. Telerehabilitation: state-of-the-art from an informatics perspective. Int J Telerehab 2009; 1: 73–84.

Peretti

Amenta

Tayebati

, et al. Telerehabilitation: review of the state-of-the-art and areas of application. JMIR Rehabil Assist Technol 2017; 4: e7.

Brennan

Tindall

Theodoros

, et al. A blueprint for telerehabilitation guidelines. Int J Telerehabil 2010; 2: 31–34.

Baroni

Jacob

MFA

Rios

, et al. The state of the art in telerehabilitation for musculoskeletal conditions. Arch Physiother 2023; 13: 1.

Jaswal

Howe

, et al. The era of technology in healthcare-an evaluation of telerehabilitation on client outcomes: a systematic review and meta-analysis. J Occup Rehabil 2024. DOI: 10.1007/s10926-024-10237-4.

10.

Camden

Pratte

Fallon

, et al. Diversity of practices in telerehabilitation for children with disabilities and effective intervention characteristics: results from a systematic review. Disabil Rehabil 2020; 42: 3424–3436.

11.

Shigekawa

Fix

Corbett

, et al. The current state of telehealth evidence: a rapid review. Health Aff 2018; 37: 1975–1982.

12.

Reifenberg

Gabrosek

Tanner

, et al. Feasibility of pediatric game-based neurorehabilitation using telehealth technologies: a case report. Am J Occup Ther 2017; 71: 1–8.

13.

Tanner

Bican

Boster

, et al. Feasibility and acceptability of clinical pediatric telerehabilitation services. Int J Telerehabil 2020; 12: 43–52.

14.

World Health Organization (WHO). WHO develops guidance to improve telemedicine services [Internet], https://www.who.int/europe/news/item/15-07-2024-who-develops-guidance-to-improve-telemedicine-services (2024, cited 11 April 2025).

15.

Telehealth in occupational therapy. Am J Occup Ther 2018; 72: 7212410059p1–18.

16.

Lee

Deutsch

Holdsworth

, et al. Telerehabilitation in physical therapist practice: a clinical practice guideline from the American physical therapy association. Phys Ther 2024; 104: pzae045.

17.

Frigerio

Del Monte

Sotgiu

, et al. Parents’ satisfaction of tele-rehabilitation for children with neurodevelopmental disabilities during the COVID-19 pandemic. BMC Prim Care 2022; 23: 146.

18.

Bezuidenhout

Joseph

Thurston

, et al. Telerehabilitation during the COVID-19 pandemic in Sweden: a survey of use and perceptions among physiotherapists treating people with neurological diseases or older adults. BMC Health Serv Res 2022; 22: 555.

19.

Bonavolontà

Cataldi

Coluccia

, et al. Sustainable intervention for health promotion and postural control improvement: effects of home-based oculomotor training. Sustainability 2020; 12: 10552.

20.

Fanuscu

Öz

Aslıyüce

YÖ

, et al. Effects of clinic-based and telerehabilitation-based motor control exercises in individuals with chronic low back pain - A randomized controlled trial with 3-month follow-up. Clin J Pain 2024; 40: 700–708. https://consensus.app/papers/effects-of-clinicbased-and-telerehabilitationbased-fanuscu-%C3%B6z/38ffd4a0174452be9a476aaafcae6c55/. cited 11 April 2025.

21.

Cumpston

McKenzie

Welch

, et al. Strengthening systematic reviews in public health: guidance in the Cochrane Handbook for Systematic Reviews of Interventions, 2nd edition. J Public Health 2022; 44: e588–e592.

22.

Page

McKenzie

Bossuyt

, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Br Med J 2021; 372: n71.

23.

Peters

MDJ

Godfrey

Khalil

, et al. Guidance for conducting systematic scoping reviews. Int J Evid Based Healthc 2015; 13: 141–146.

24.

Schiavo

. PROSPERO: an international register of systematic review protocols. Med Ref Serv Q 2019; 38: 171–180.

25.

Ouzzani

Hammady

Fedorowicz

, et al. Rayyan—a web and mobile app for systematic reviews. Syst Rev 2016; 5: 210.

26.

Preston

Weightman

Gallagher

, et al. A pilot single-blind multicentre randomized controlled trial to evaluate the potential benefits of computer-assisted arm rehabilitation gaming technology on the arm function of children with spastic cerebral palsy. Clin Rehabil 2016; 30: 1004–1015.

27.

Wang

Zhang

, et al. Rehabilitation for spinal muscular atrophy patients in China: a national cross-sectional study. Orphanet J Rare Dis 2024; 19: 279.

28.

Chen

Garcia-Vergara

Howard

. Effect of a home-based virtual reality intervention for children with cerebral palsy using super pop VR evaluation metrics: a feasibility study. Rehabil Res Pract 2015; 2015: 1–9.

29.

Sgandurra

Cecchi

Beani

, et al. Tele-UPCAT: study protocol of a randomised controlled trial of a home-based tele-monitored UPper limb children action observation training for participants with unilateral cerebral palsy. BMJ Open 2018; 8: e017819.

30.

Farr

Green

Bremner

, et al. Feasibility of a randomised controlled trial to evaluate home-based virtual reality therapy in children with cerebral palsy. Disabil Rehabil 2021; 43: 85–97.

31.

James

Ziviani

Ware

, et al. Randomized controlled trial of web-based multimodal therapy for unilateral cerebral palsy to improve occupational performance. Dev Med Child Neurol 2015; 57: 530–538.

32.

Himmelmann

Beckung

Hagberg

, et al. Gross and fine motor function and accompanying impairments in cerebral palsy. Dev Med Child Neurol 2006; 48: 417–423.

33.

Kwon

Kim

, et al. Relationship between gross motor function and daily functional skill in children with cerebral palsy. Ann Rehabil Med 2013; 37: 41–49.

34.

Macchitella

Accogli

Barraco

, et al. A two-step neurorehabilitation program utilizing extended reality and telerehabilitation for children with cerebral palsy: a pilot study on effectiveness. adherence, and technical feasibility. Appl Sci (Basel) 2024; 14: 11961.

35.

Chen

Ren

, et al. Home-Based versus laboratory-based robotic ankle training for children with cerebral palsy: a pilot randomized comparative trial. Arch Phys Med Rehabil 2016; 97: 1237–1243.

36.

Selanon

Chuangchai

. Walking activity increases physical abilities and subjective health in people with seven different types of disabilities. Front Public Health [Internet] 2023; 11. https://www.frontiersin.orghttps://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2023.1120926/full

37.

Eek

Beckung

. Walking ability is related to muscle strength in children with cerebral palsy. Gait Posture 2008; 28: 366–371.

38.

Kassee

Hunt

Holmes

MWR

, et al. Home-based Nintendo Wii training to improve upper-limb function in children ages 7 to 12 with spastic hemiplegic cerebral palsy. J Pediatr Rehabil Med 2017; 10: 145–154.

39.

MacIntosh

Desailly

Vignais

, et al. A biofeedback-enhanced therapeutic exercise video game intervention for young people with cerebral palsy: a randomized single-case experimental design feasibility study. PLoS One 2020; 15: e0234767.

40.

Calabrò

Mojdehdehbaher

. AI-Driven Telerehabilitation: benefits and challenges of a transformative healthcare approach. AI 2025; 6: 62.

41.

Pramuka

Van Roosmalen

. Telerehabilitation technologies: accessibility and usability. Int J Telerehab 2009; 1: 85–98.

42.

Mumtaz

Riaz

Wajid

, et al. Current challenges and potential solutions to the use of digital health technologies in evidence generation: a narrative review. Front Digit Health 2023; 5: 1203945.

43.

Suso-Martí

La Touche

Herranz-Gómez

, et al. Effectiveness of telerehabilitation in physical therapist practice: an Umbrella and mapping review with meta–meta-analysis. Phys Ther 2021; 101: pzab075.

44.

Nicolas

Leblong

Fraudet

, et al. Telerehabilitation solutions in patient pathways: an overview of systematic reviews. Digit Health 2024; 10: 20552076241294110.

45.

Krasovsky

Weiss

Gafni-Lachter

, et al. Hybrid approaches to allied health services for children and young people: a scoping review. J NeuroEngineering Rehabil 2024; 21: 122.

46.

Rasa

. Artificial intelligence and its revolutionary role in physical and mental rehabilitation: a review of recent advancements. Biomed Res Int 2024; 2024: 9554590.

47.

McConnochie

Fox

Bellenger

, et al. LiDAR-based scaling of OpenSim musculoskeletal human models is a viable alternative to marker-based approaches - A preliminary study. J Biomech 2025; 179: 112439.

48.

Almujally

Khan

Al Mudawi

, et al. Biosensor-driven IoT wearables for accurate body motion tracking and localization. Sensors 2024; 24: 3032.

49.

González-González

Toledo-Delgado

Muñoz-Cruz

, et al. Serious games for rehabilitation: gestural interaction in personalized gamified exercises through a recommender system. J Biomed Inform 2019; 97: 103266.

50.

Aderinto

Olatunji

Abdulbasit

, et al. Exploring the efficacy of virtual reality-based rehabilitation in stroke: a narrative review of current evidence. Ann Med 2023; 55: 2285907.

51.

Abedi

Colella

TJF

Pakosh

, et al. Artificial intelligence-driven virtual rehabilitation for people living in the community: a scoping review. npj Digit Med 2024; 7: 25.

52.

Onciul

Tataru

Dumitru

, et al. Artificial intelligence and neuroscience: transformative synergies in brain research and clinical applications. J Clin Med 2025; 14: 550.

Supplementary Material

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