A Random Tree Forest decision support system to personalize upper extremity robot-assisted rehabilitation in stroke: a pilot study

Rehabilitation with and Without Robot and Allied Digital Technologies (RADTs) in Stroke Patients: A Study Protocol for a Multicentre Randomised Controlled Trial on the Effectiveness, Acceptability, Usability, and Economic-Organisational Sustainability of RADTs from Subacute to Chronic Phase (STROKEFIT4)

Background: Rehabilitation after stroke often employs Robots and Allied Digital Technologies (RADTs). However, evidence of their effectiveness remains inconclusive due to study heterogeneity and limited sample sizes. Methods: This is a protocol of a pragmatic multicentre, multimodal, randomised, controlled, parallel-group (1:1) interventional study with blinded assessors aimed at assessing the effectiveness and sustainability of RADT-mediated rehabilitation compared to traditional rehabilitation. The trial will recruit 596 adult subacute post-stroke patients. Participants will be randomised into either the experimental group (using RADTs and two therapists supervising four to six patients) or the control group (individual traditional rehabilitation). Patients in both groups will undergo a comprehensive rehabilitation treatment, targeting (a) upper limb sensorimotor abilities; (b) lower limb sensorimotor abilities and gait; (c) balance; and (d) cognitive abilities. Patients will undergo 25 sessions, each lasting 45 min, with a frequency of 5 (inpatients) or 3 (outpatients) times a week. The primary endpoint is the non-inferiority of RADTs in the recovery of the activities of daily living (ADL) using the modified Barthel Index. If non-inferiority is established, the study will evaluate the superiority. Secondary endpoints will analyse the improvements in the aforementioned domains, as well as changes in neural plasticity and biochemical aspects. Upper limb dexterity and gait recovery rates during treatment will be monitored. The study will also evaluate ADL and quality of life during a six-month follow-up period. Acceptability and usability of integrated RADTs-based rehabilitation for patients, families, and healthcare providers, along with economic and organisational sustainability for patients, payers, and society, will also be assessed. Conclusions: This study aims to establish stronger evidence on the effectiveness of RADTs in post-stroke patients. Trial registration number: NCT06547827.

Artificial intelligence in stroke risk assessment and management via retinal imaging

Retinal imaging, used for assessing stroke-related retinal changes, is a non-invasive and cost-effective method that can be enhanced by machine learning and deep learning algorithms, showing promise in early disease detection, severity grading, and prognostic evaluation in stroke patients. This review explores the role of artificial intelligence (AI) in stroke patient care, focusing on retinal imaging integration into clinical workflows. Retinal imaging has revealed several microvascular changes, including a decrease in the central retinal artery diameter and an increase in the central retinal vein diameter, both of which are associated with lacunar stroke and intracranial hemorrhage. Additionally, microvascular changes, such as arteriovenous nicking, increased vessel tortuosity, enhanced arteriolar light reflex, decreased retinal fractals, and thinning of retinal nerve fiber layer are also reported to be associated with higher stroke risk. AI models, such as Xception and EfficientNet, have demonstrated accuracy comparable to traditional stroke risk scoring systems in predicting stroke risk. For stroke diagnosis, models like Inception, ResNet, and VGG, alongside machine learning classifiers, have shown high efficacy in distinguishing stroke patients from healthy individuals using retinal imaging. Moreover, a random forest model effectively distinguished between ischemic and hemorrhagic stroke subtypes based on retinal features, showing superior predictive performance compared to traditional clinical characteristics. Additionally, a support vector machine model has achieved high classification accuracy in assessing pial collateral status. Despite this advancements, challenges such as the lack of standardized protocols for imaging modalities, hesitance in trusting AI-generated predictions, insufficient integration of retinal imaging data with electronic health records, the need for validation across diverse populations, and ethical and regulatory concerns persist. Future efforts must focus on validating AI models across diverse populations, ensuring algorithm transparency, and addressing ethical and regulatory issues to enable broader implementation. Overcoming these barriers will be essential for translating this technology into personalized stroke care and improving patient outcomes.

Enhancing Motor Function and Quality of Life Combining Advanced Robotics and Biomechatronics in an Adult with Dystonic Spastic Tetraparesis: A Case Report

This case report explores the innovative integration of robotic and biomechatronic technologies, including the Motore and Ultra+ devices and neuro-suits, in a 10-session rehabilitation program for a young adult with dystonic spastic tetraparesis. Notable improvements were observed in upper limb motor function, coordination, and quality of life as measured by an increase of 18 pints on the Fugl-Meyer scale and a 25% improvement in the Bartle Index. Range of motion measurements showed consistent improvements, with task execution times improving by 10 s. These findings suggest the potential of combining wearable, robotic, and biomechatronic systems to enhance neurorehabilitation. Further refinement of these technologies might support clinicians in maximizing their integration in therapeutics, despite technical issues like synchronization issues that must be overcome.

Efficacy of telerehabilitation with digital and robotic tools for the continuity of care of people with chronic neurological disorders: The TELENEURO@REHAB protocol for a randomized controlled trial

Context

Chronic Neurological Disorders (CNDs) are among the leading causes of disability worldwide, and their contribution to the overall need for rehabilitation is increasing. Therefore, the identification of new digital solutions to ensure early and continuous care is mandatory.

Objective

This protocol proposes to test the usability, acceptability, safety, and efficacy of Telerehabilitation (TR) protocols with digital and robotic tools in reducing the perceived level of disability in CNDs including Parkinson's Disease (PD), Multiple Sclerosis (MS), and post-stroke patients.

Design Setting and Subjects

This single-blinded, multi-site, randomized, two-treatment arms controlled clinical trial will involve PD (N = 30), MS (N = 30), and post-stroke (N = 30). Each participant will be randomized (1:1) to the experimental group (20 sessions of motor telerehabilitation with digital and robotic tools) or the active control group (20 home-based motor rehabilitation sessions according to the usual care treatment). Primary and secondary outcome measures will be obtained at the baseline (T0), post-intervention (T1, 5 weeks after baseline), and at follow-up (T2, 2 months after treatment).

Main Outcome Measures

a multifaceted evaluation including quality of life, motor, and clinical/functional measures will be conducted at each time-point of assessment. The primary outcome measures will be the change in the perceived level of disability as measured by the World Health Organization Disability Assessment Schedule 2.0.

Conclusion

The implementation of TR protocols will enable a more targeted and effective response to the growing need for rehabilitation linked to CNDs, ensuring accessibility to rehabilitation services from the initial stages of the disease.

A Decision Support System to Provide an Ongoing Prediction of Robot-Assisted Rehabilitation Outcome in Stroke Survivors

Clinicians often deal with complex robotic platform and serious games in stroke patients rehabilitation contexts, and they face two main problems: 1) the interpretation of either the performance in game or measures of a robotic system from the motor recovery point of view, and 2) the duration and complexity of clinical scales administration that makes repetitive assessments during the therapy unpractical. In this paper, a Random Tree Forest based system was trained and tested to provide a prediction of different clinical outcomes (i.e. FMA, ARAT, and MI) along the whole therapy duration, having non-clinical measures only as inputs, acting as a simulated decision support system. The dataset includes 30 post-stroke patients, that underwent a 30-session robot-assisted rehabilitation treatment. Results have shown that the system is able to produce very accurate and reliable predictions about the motor recovery of the patient at the end of the therapy, already in the first phases of the rehabilitation (i40% of therapy execution), just using robotic platform measures. Such a tool would provide a great benefit in terms of rehabilitation objectives planning, as a decision support tool for highly personalized rehabilitation treatments.

Tailoring Upper-Limb Robot-Aided Orthopedic Rehabilitation on Patients' Psychophysiological State

Physical therapy keeps exploiting more and more the capabilities of the robot of adapting the treatments to patients' needs. This paper aims at presenting a psychophysiological-aware control strategy for upper limb robot-aided orthopedic rehabilitation. The main features are the capability of i) generating point-to-point trajectories inside an adaptable workspace, ii) providing assistance in guiding the patients' limbs in accomplishing the assigned task allowing them to freely move with a certain degree of spatial and temporal autonomy and iii) tuning the control parameters according to the patients' kinematics performance and psychophysiological state. The implemented control strategy is validated in a real clinical setting on eight orthopedic patients undergoing twenty daily robot-aided rehabilitation sessions. The psychophysiological-aware control strategy evidenced a positive impact on the enrolled participants since they are effectively conducted in a calmer condition with respect to the patients who did not receive the psychophysiological adaptation. Moreover, clinical performance indicators suggest that the proposed tailored control strategy improves motor functions.

Treatment Outcome Prediction Using Multi-Task Learning: Application to Botulinum Toxin in Gait Rehabilitation

We propose a framework for optimizing personalized treatment outcomes for patients with neurological diseases. A typical consequence of such diseases is gait disorders, partially explained by command and muscle tone problems associated with spasticity. Intramuscular injection of botulinum toxin type A is a common treatment for spasticity. According to the patient's profile, offering the optimal treatment combined with the highest possible benefit-risk ratio is important. For the prediction of knee and ankle kinematics after botulinum toxin type A (BTX-A) treatment, we propose: (1) a regression strategy based on a multi-task architecture composed of LSTM models; (2) to introduce medical treatment data (MTD) for context modeling; and (3) a gating mechanism to model treatment interaction more efficiently. The proposed models were compared with and without metadata describing treatments and with serial models. Multi-task learning (MTL) achieved the lowest root-mean-squared error (RMSE) (5.60°) for traumatic brain injury (TBI) patients on knee trajectories and the lowest RMSE (3.77°) for cerebral palsy (CP) patients on ankle trajectories, with only a difference of 5.60° between actual and predicted. Overall, the best RMSE ranged from 5.24° to 6.24° for the MTL models. To the best of our knowledge, this is the first time that MTL has been used for post-treatment gait trajectory prediction. The MTL models outperformed the serial models, particularly when introducing treatment metadata. The gating mechanism is efficient in modeling treatment interaction and improving trajectory prediction.