Stroke is one of the main causes of long-term disabilities, increasing the cost of national healthcare systems due to the elevated costs of rigorous treatment that is required, as well as personal cost because of the decreased ability of the patient to work. Traditional rehabilitation strategies rely heavily on individual clinical data and the caregiver’s experience to evaluate the patient and not in data extracted from population data. The use of machine learning (ML) algorithms can offer evaluation tools that will lead to new personalized interventions. The aim of this scoping review is to introduce the reader to key directions of ML techniques for the prediction of functional outcomes in stroke rehabilitation and identify future scientific research directions. The search of the relevant literature was performed using PubMed and Semantic Scholar online databases. Full-text articles were included if they focused on ML in predicting the functional outcome of stroke rehabilitation. A total of 26 out of the 265 articles met our inclusion criteria. The selected studies included ML approaches and were directly related to the inclusion criteria. ML can play a key role in supporting decision making during pre- and post-treatment interventions for post-stroke survivors, by utilizing multidisciplinary data sources.

Stroke is one of the leading causes of long-term disabilities in motor and cognition functionality. An early and accurate prediction of rehabilitation outcomes can lead to a tailor-made treatment that can significantly improve the post-stroke quality of life of a person. This scoping review aimed to summarize studies that use Artificial Intelligence (AI) for the prediction of language and cognition rehabilitation outcomes and the need to use AI in this domain. This study followed the PRISMA-ScR guidelines for two databases, Scopus and PubMed. The results, which are measured with several metrics depending on the task, regression, or classification, present encouraging outcomes as they can predict the cognitive functionality of post-stroke patients with relative precision. Among the results of the paper are the identification of the most effective Machine Learning (ML) algorithms, and the identification of the key factors that influence rehabilitation outcomes. The majority of studies focus on aphasia and present high performance achieving up to 97% recall and 91.4% precision. The main limitations of the studies were the small subject population and the lack of an external dataset. However, effective ML algorithms along with explainability are expected to become among the most prominent solutions for precision medicine due to their ability to overcome non-linearities on data and provide insights and transparent predictions that can help healthcare professionals make more informed and accurate decisions.

Background and Objectives: The COVID-19 pandemic brought significant challenges across governmental, industrial, and social systems. Additionally, numerous studies have reported a sharp increase in both mental health issues and musculoskeletal disorders worldwide. This study aimed to investigate whether a specific exercise program could alleviate neck and upper back pain symptoms and improve mood state among healthcare and office employees during the post-COVID-19 period. Materials and Methods: This was an unblinded (open label) randomized controlled trial (both the participants and the researchers knew which treatment or intervention was being administered). In total, 40 healthcare employees from a public hospital and 98 remote office employees, all of whom reported neck and upper back pain, were randomly assigned to an experimental (EG) and control group (CG). The experimental groups underwent the same exercise protocol, while their corresponding control groups underwent the assessments only. Pain assessment using the Visual Analogue Scale (VAS) and mood state using the Feeling Scale (FS) questionnaires of the participants were recorded at baseline (pre-intervention) and immediately following the exercise intervention period of 6 weeks (post-intervention). The intervention consisted of 18 exercise sessions conducted over a 6-week period, with remote office workers participating online and healthcare workers attending exercise sessions on site (at the hospital). Statistical analysis was performed using non-parametric Mann–Whitney and Wilcoxon tests, as appropriate. Results: Following the six-week intervention, the percentage of workers in the EG reporting pain decreased significantly (from 75% to 45% for healthcare workers and from 54% to 25% for office employees), whereas no change was observed for their corresponding control groups. Similarly, the mood state of the EGs significantly improved compared with the control groups, as measured between pre- and post-intervention sessions. Conclusions: The COVID-19 period exacerbated stress and musculoskeletal strain, particularly for workers in demanding occupational roles. To mitigate these effects, exercise programs that can be applied while at work should be implemented, as they are effective in enhancing mood and managing neck pain in employees with physical exertion.