Jeong-Ho Park1, Yushin Kim2, Kwang-Jae Lee3, Yong-Soon Yoon3, Si Hyun Kang4, Heesang Kim4, Hyung-Soon Park5. 1. Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea. 2. Division of Health Administration and Healthcare, Cheongju University, Cheongju, Korea. 3. Department of Rehabilitation Medicine, Presbyterian Medical Center, Jeonju, Korea. 4. Department of Physical Medicine and Rehabilitation, Chung-Ang University College of Medicine, Seoul, Korea. 5. Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea. Electronic address: hyungspark@kaist.ac.kr.
Abstract
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.
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.
Authors: Mike Jones; George Collier; David J Reinkensmeyer; Frank DeRuyter; John Dzivak; Daniel Zondervan; John Morris Journal: Int J Environ Res Public Health Date: 2020-01-24 Impact factor: 3.390