Correlation between Harris hip score and gait analysis through artificial intelligence pose estimation in patients after total hip arthroplasty

Abstract

Background: Recently, open pose estimation using artificial intelligence (AI) has enabled the analysis of time series of human movements through digital video inputs. Analyzing a person's actual movement as a digitized image would give objectivity in evaluating a person's physical function. In the present study, we investigated the relationship of AI camera-based open pose estimation with Harris Hip Score (HHS) developed for patient-reported outcome (PRO) of hip joint function. Method: HHS evaluation and pose estimation using AI camera were performed for a total of 56 patients after total hip arthroplasty in Gyeongsang National University Hospital. Joint angles and gait parameters were analyzed by extracting joint points from time-series data of the patient's movements. A total of 65 parameters were from raw data of the lower extremity. Principal component analysis (PCA) was used to find main parameters. K-means cluster, X-squared test, Random forest, and mean decrease Gini (MDG) graph were also applied. Results: The train model showed 75% prediction accuracy and the test model showed 81.8% reality prediction accuracy in Random forest. “Anklerang_max”, “kneeankle_diff”, and “anklerang_rl” showed the top 3 Gini importance score in the Mean Decrease Gini (MDG) graph. Conclusion: The present study shows that pose estimation data using AI camera is related to HHS by presenting associated gait parameters. In addition, our results suggest that ankle angle associated parameters could be key factors of gait analysis in patients who undergo total hip arthroplasty.

Keywords

• Harris hip score
• Open pose estimation
• Artificial intelligence
• Patient-reported outcome