Component positioning is of great importance in total hip arthroplasty (THA) and navigation systems can help guide surgeons in the optimal placement of the implants. We report on a newly developed navigation system which employs an inertial measurement unit (IMU) to measure acetabular cup inclination and anteversion. To assess the accuracy of the IMU when used for acetabular cup placement and compare this with an established optical navigation system (ONS).Introduction
Aims
Acetabular cup position is an important factor in successful total hip arthroplasty (THA). Optimal cup placement requires surgeons to possess an accurate perception of pelvic orientation during cup impaction, however, varying pelvic anatomy and limited visual cues in the surgical field may interfere with this process. The purpose of this study was to evaluate the utility of an inertial measurement unit (IMU) in monitoring pelvic position during THA. Ten patients scheduled to undergo THA were IRB-approved and consented by four surgeons. A small IMU was placed over the patient's sacrum pre-operatively and zeroed in standing position. Pelvic orientation data was streamed and captured wirelessly throughout the procedure. Surgeons were blinded to all data throughout the study period. Prior to cup impaction, the surgeon indicated his intended cup abduction angle and the degree to which the cup impactor was manipulated to compensate for perceived AP pelvic tilt. The degree of pelvic tilt as determined by the IMU (angle β) was then recorded (Figure 1). AP-pelvis radiographs were measured in Martell Hip Analysis Suite post-operatively to calculate the cup abduction angle, which was then compared to the surgeon's intended abduction angle to determine surgeon accuracy. To predict the final cup abduction angle, the degree of pelvic tilt recorded by the IMU (angle β) was subtracted from the abduction angle of the cup impactor (angle α) that was positioned using the OR table as a reference (Figure 1). This value was then compared to the measured post-operative cup abduction angle in order to assess the accuracy of the IMU in measuring pelvic tilt. Surgeon accuracy and IMU accuracy were compared to determine if the IMU was more or less effective than surgeon perception at determining pelvic tilt.Introduction:
Materials & Methods:
An objective technological solution for tracking adherence to at-home shoulder physiotherapy is important for improving patient engagement and rehabilitation outcomes, but remains a significant challenge. The aim of this research was to evaluate performance of machine-learning (ML) methodologies for detecting and classifying inertial data collected during in-clinic and at-home shoulder physiotherapy exercise. A smartwatch was used to collect inertial data from 42 patients performing shoulder physiotherapy exercises for rotator cuff injuries in both in-clinic and at-home settings. A two-stage ML approach was used to detect out-of-distribution (OOD) data (to remove non-exercise data) and subsequently for classification of exercises. We evaluated the performance impact of grouping exercises by motion type, inclusion of non-exercise data for algorithm training, and a patient-specific approach to exercise classification. Algorithm performance was evaluated using both in-clinic and at-home data.Aims
Methods
Abstract. Rehabilitation exercise is critical for patients’ recovery after knee injury or post-surgery. Unfortunately, adherence to exercise is low due to a lack of positive feedback and poor self-motivation. Therefore, it is crucial to monitor their progress and provide supervision.
Introduction. Robotic-assisted total knee arthroplasty (TKA) has demonstrated significant benefits, including improved accuracy of component positioning compared to conventional jig-based TKA. However, previous studies have often failed to associate these findings with clinically significant improvements in patient-reported outcome measures (PROMs).
