Assessment of Automated Identification of Phases in Videos of Total Hip Arthroplasty Using Deep Learning Techniques.

Background: As the population ages, the rates of hip diseases and fragility fractures are increasing, making total hip arthroplasty (THA) one of the best methods for treating elderly patients. With the increasing number of THA surgeries and diverse surgical methods, there is a need for standard evaluation protocols. This study aimed to use deep learning algorithms to classify THA videos and evaluate the accuracy of the labelling of these videos. Methods: In our study, we manually annotated 7 phases in THA, including skin incision, broaching, exposure of acetabulum, acetabular reaming, acetabular cup positioning, femoral stem insertion, and skin closure. Within each phase, a second trained annotator marked the beginning and end of instrument usages, such as the skin blade, forceps, Bovie, suction device, suture material, retractor, rasp, femoral stem, acetabular reamer, head trial, and real head. Results: In our study, we utilized YOLOv3 to collect 540 operating images of THA procedures and create a scene annotation model. The results of our study showed relatively high accuracy in the clear classification of surgical techniques such as skin incision and closure, broaching, acetabular reaming, and femoral stem insertion, with a mean average precision (mAP) of 0.75 or higher. Most of the equipment showed good accuracy of mAP 0.7 or higher, except for the suction device, suture material, and retractor. Conclusions: Scene annotation for the instrument and phases in THA using deep learning techniques may provide potentially useful tools for subsequent documentation, assessment of skills, and feedback.

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

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.

Accelerometer-derived physical activity analysis of elderly osteoarthritis patients.

BACKGROUND: Because disability in Osteoarthritis (OA) may change physical activity (PA), which might affect the disease progression, it is important to measure a patient's daily PA to study the relationship between a patient's PA and disease progression. OBJECTIVE: The objective of the present study was to investigate the relationship between PA and patients with OA and people without OA using data from the Korea National Health and Nutrition Examination Survey (KNHANES). METHODS: Demographic study was conducted to obtain data of comorbidities of participants. PA was compared between the group with OA (OA group) and the group without OA (non-OA group). In addition, PAs of OA patients with comorbidities and those without comorbidities were compared. The cut-off of moderate to vigorous physical activity (MVPA) was obtained through a receiver operating characteristic (ROC) curve. RESULTS: In the demographic study, there were significantly more educated participants in the OA group (p < .001). Actigraph data showed a significant decrease in MVPA (p < .001) but a significant increase in light activity (p = .002) in the OA group. In addition, the OA group showed significantly lower light PA but significantly higher MVPA in ≥10 min bout length. OA patients with comorbidities showed higher MVPA than OA patients without comorbidities (p = .044). The cut-off point of MVPA was 7.071 min/day when ROC curve was conducted. CONCLUSIONS: The present study suggests that patients with OA and low activity need a certain level of physical activity and a cut-off point for MVPA is presented which accounts for comorbidities in OA patients.