AI Article Synopsis
- A new artificial neural network (ANN) algorithm was developed to assess rehabilitation by analyzing clinical gait data across various patient demographics, including age and disease type.
- The trained ANN successfully matched human evaluations in 82.2% of cases, indicating strong reliability, with a Cohen's kappa score of 0.743.
- There is a significant correlation (0.825, p < 0.01) between the ANN's assessments and improved Ashworth scores given by human evaluators, suggesting its potential utility in clinical rehabilitation.
Article Abstract
In order to explore the application of artificial neural network in rehabilitation evaluation, a kind of ANN stable and reliable artificial intelligence algorithm is proposed. By learning the existing clinical gait data, this method extracted the gait characteristic parameters of patients with different ages, disease types and course of disease, and repeated data iteration and finally simulated the corresponding gait parameters of patients. Experiments showed that the trained ANN had the same score as the human for most of the data (82.2%, Cohen's kappa = 0.743). There was a strong correlation between ANN and improved Ashworth scores as assessed by human raters ( = 0.825, < 0.01). As a stable and reliable artificial intelligence algorithm, ANN can provide new ideas and methods for clinical rehabilitation evaluation.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8523250 | PMC |
| http://dx.doi.org/10.1155/2021/3959844 | DOI Listing |
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