Artificial Intelligence machine learning and conventional physical therapy for upper limb outcome in patients with stroke: a systematic review and meta-analysis

OBJECTIVE

The goal of this study was to compare the effect of different artificial intelligence (AI) machine learning and conventional therapy (CT) on upper limb impairments in patients with stroke.

MATERIALS AND METHODS

PubMed, PubMed Central, Google Scholar, MEDLINE, Cochrane Library, Web of Science, Research Gate, and Wiley Online Library were searched. Descriptive statistics about variables were reported to calculate standardized mean differences in outcomes of motor control (the primary outcome), functional independence, upper extremity performance, and muscle tone. The Physiotherapy Evidence Database (PEDro) Scale was used to assess qualitative papers. The primary outcomes of AI and CT have been included in the meta-analyses.

RESULTS

Ten papers with a total of 481 stroke patients were included and upper limb rehabilitation, upper limb functioning, and basic manual dexterity were examined. The heterogeneity test of the whole included measures (I2=45%) was medium. There were significant differences between the included measures (p-value=0.03) with a total SMD of 0.10 [0.01, 0.19]. According to the test for subgroup difference, it was found that there was a highly significant difference between the subgroups of the included measures (p-value=0.01) and the heterogeneity test (I2=59.8%).

CONCLUSIONS

AI is a feasible and safe method in post-stroke rehabilitation and improves upper-extremity function compared to CT. Significant AI post-treatment effects on upper-limb impairments have been observed. The findings showed that higher-quality evidence was detected in six assessment scales. However, a lower quality of evidence was detected in other scales. This indicated large or very large and consistent estimates of the treatment effects, and researchers were confident about the results. Therefore, the included observational studies are likely to provide an overestimate of the true effect.

Controlled release of phosphorous fertilizer bound to carboxymethyl starch-g-polyacrylamide and maintaining a hydration level for the plant

A controlled release fertilizer system based on phosphate bound-carboxymethyl starch-graft-polyacrylamide (P-CMS-g-PAM) has been prepared in order to deliver the phosphate fertilizer to the plant at a constant rate thereby. This system aims to increase fertilizer phosphorus use efficiency (FPUE) and maintain a hydration level for the plant at the same time. Two types of starch phosphate monoesters were prepared using mono-ammonium (MAP) and di-ammonium phosphate (DAP). First, starch was converted to carboxymethyl starch and then was phosphorylated with mono-ammonium dihydrogen phosphate and di-ammonium dihydrogen phosphate. After phosphorylation, the samples were grafted with acrylamide in the presence of methylene bisacrylamide as a crosslinking agent. The prepared systems of P-CMS-g-PAM were differently characterized by Fourier transformer infrared (FT-IR), thermogravimetric analysis (TGA), scanning electron microscope (SEM) and phosphorous analysis. The swelling behavior was investigated. The kinetics of grafting reaction were also studied. The release behavior of phosphate bound was studied at pH 7 and 25 °C. The mechanism of the phosphate release from P-CMS-g-PAM was examined using the Korsmeyer-Peppas model.