Use of artificial intelligence as an instrument of evaluation after stroke: a scoping review based on international classification of functioning, disability and health concept.

INTRODUCTION: To understand the current practices in stroke evaluation, the main clinical decision support system and artificial intelligence (AI) technologies need to be understood to assist the therapist in obtaining better insights about impairments and level of activity and participation in persons with stroke during rehabilitation. METHODS: This scoping review maps the use of AI for the functional evaluation of persons with stroke; the context involves any setting of rehabilitation. Data were extracted from CENTRAL, MEDLINE, EMBASE, LILACS, CINAHL, PEDRO Web of Science, IEEE Xplore, AAAI Publications, ACM Digital Library, MathSciNet, and arXiv up to January 2021. The data obtained from the literature review were summarized in a single dataset in which each reference paper was considered as an instance, and the study characteristics were considered as attributes. The attributes used for the multiple correspondence analysis were publication year, study type, sample size, age, stroke phase, stroke type, functional status, AI type, and AI function. RESULTS: Forty-four studies were included. The analysis showed that spasticity analysis based on ML techniques was used for the cases of stroke with moderate functional status. The techniques of deep learning and pressure sensors were used for gait analysis. Machine learning techniques and algorithms were used for upper limb and reaching analyses. The inertial measurement unit technique was applied in studies where the functional status was between mild and severe. The fuzzy logic technique was used for activity classifiers. CONCLUSION: The prevailing research themes demonstrated the growing utility of AI algorithms for stroke evaluation.

Altered Visual and Proprioceptive Spatial Perception in Individuals with Parkinson's Disease.

Difficulties in the integration of visual, vestibular, and somatosensory information in individuals with Parkinson's disease (PD) may alter perception of verticality. Accordingly, in this cross-sectional study, we analyzed PD patients' (n = 13) subjective visual vertical (SVV) and subjective haptic vertical (SHV) perceptions and compared them to those of healthy controls (n = 14). We compared SVV and SHV findings among participants with PD, healthy controls, and cutoff points of normality based on prior research literature, using the parametric nonpaired t test (at p < .05) and Cohen's d (at d > 0.8) to determine clinical relevance. We analyzed SVV with the bucket test and SHV with the rod rotations task in clockwise and counterclockwise directions. We calculated Pearson correlations to analyze the association between verticality tests and the most clinically affected body side. We calculated both the percentage of A-effect (expression of body tilt underestimation to the midline) and E-effect (expression of body tilt overestimation in the upright position). Individuals with PD showed greater variability in right SHV supination compared to the healthy control participants (p = .002). There was greater clinical relevance in right (as opposed to left) SVV (d = 0.83), right (as opposed to left) SHV pronation (d = 0.91), and left (as opposed to right) SHV pronation (d = 0.88). We observed a higher proportion of E-effect in individuals with PD. A significantly higher proportion of patients with PD, compared to patients in past literature, had right SHV pronation (p = .001), left SHV pronation (p = .023), right SHV supination (p = .001), left SHV supination (p = .046), and left SHV pronation (p = .046). Thus, subjective visual and proprioceptive perception of verticality is altered in patients with PD, compared to individuals without PD.