Artificial Neural Network Learns Clinical Assessment of Spasticity in Modified Ashworth Scale.

OBJECTIVE

To propose an artificial intelligence (AI)-based decision-making rule in modified Ashworth scale (MAS) that draws maximum agreement from multiple human raters and to analyze how various biomechanical parameters affect scores in MAS.

DESIGN

Prospective observational study.

SETTING

Two university hospitals.

PARTICIPANTS

Hemiplegic adults with elbow flexor spasticity due to acquired brain injury (N=34).

INTERVENTION: Not applicable.

MAIN OUTCOME MEASURES: Twenty-eight rehabilitation doctors and occupational therapists examined MAS of elbow flexors in 34 subjects with hemiplegia due to acquired brain injury while the MAS score and biomechanical data (ie, joint motion and resistance) were collected. Nine biomechanical parameters that quantify spastic response described by the joint motion and resistance were calculated. An AI algorithm (or artificial neural network) was trained to predict the MAS score from the parameters. Afterwards, the contribution of each parameter for determining MAS scores was analyzed.

RESULTS

The trained AI agreed with the human raters for the majority (82.2%, Cohen's kappa=0.743) of data. The MAS scores chosen by the AI and human raters showed a strong correlation (correlation coefficient=0.825). Each biomechanical parameter contributed differently to the different MAS scores. Overall, angle of catch, maximum stretching speed, and maximum resistance were the most relevant parameters that affected the AI decision.

CONCLUSIONS

AI can successfully learn clinical assessment of spasticity with good agreement with multiple human raters. In addition, we could analyze which factors of spastic response are considered important by the human raters in assessing spasticity by observing how AI learns the expert decision. It should be noted that few data were collected for MAS3; the results and analysis related to MAS3 therefore have limited supporting evidence.