Patient-reported outcome measures (PROMs) have failed to highlight differences in function or outcome when comparing knee replacement designs and implantation techniques. Ankle-worn inertial measurement units (IMUs) can be used to remotely measure and monitor the bi-lateral impact load of patients, augmenting traditional PROMs with objective data. The aim of this study was to compare IMU-based impact loads with PROMs in patients who had undergone conventional total knee arthroplasty (TKA), unicompartmental knee arthroplasty (UKA), and robotic-assisted TKA (RA-TKA). 77 patients undergoing primary knee arthroplasty (29 RA-TKA, 37 TKA, and 11 UKA) for osteoarthritis were prospectively enrolled.
Introduction. Measured outcomes from knee joint arthroplasty (TKA) have primarily focused on surgeon-directed criteria, such as alignment, range of motion measured in the clinic, and implant durability, rather than on functional outcomes. There is strong evidence that subjective reporting by patients fails to capture objective real-life function. 1,2. We believe that the recent emphasis on clinical outcomes desired by the patient, as well as the need to demonstrate value, requires a new approach to patient outcomes that directly monitors ambulatory activity after surgery. We have developed and tested a system that: 1) autonomously identifies patients who are not progressing well in their recovery from TKA surgery; 2) characterizes patient activity profiles; 3) automatically alerts health care providers of patients who should be seen for additional follow-up. We anticipate that such a system could decrease secondary procedures such as manipulation under anesthesia (MUA) and reduce hospital re-admission rates thereby resulting in significant cost savings to the patient, the care providers, and insurers. Methods. The components of the system include: 1) A sensor package that is mounted correctly in relation to the knee joint (Figure 1a) and is suitable for long term use; 2) An application that runs under the Android operating system to communicate with the sensor and to gather subjective information (pain, satisfaction, perceived stability etc. together with a photograph of the surgical site (Figure 1b); 3) Software to upload the data from the phone to a remote server; 4) An analysis and reporting package that generates, among other metrics, a profile describing the patient's activity throughout the day, trends in the recovery process, and alerts for abnormal findings (Figure 1c). The system was pilot tested on 12 patients (7 females) who underwent TKA. Complete days of data collection were scheduled for each patient every two weeks until 12 weeks, starting during the second week after surgery. Results. Patients tolerated the system well and datasets of up to 13 hours long were recorded. There was a considerable variation between patients in the use of the prosthetic knee joint at a given time point after surgery. At 6 weeks post-surgery, for example, some relatively inactive subjects had less than 50 excursions per hour while active subjects exhibited more than 750 excursions per hour. It was notable that, in activities of daily living, subjects rarely used the extremes of the flexion range that had been measured during post-operative clinic visits. Examples of activity recognition during free-living will be presented. Discussion. A
Introduction. Gait analysis systems have enjoyed increasing usage and have been validated to provide highly accurate assessments for range of motion. Size, cost, need for marker placement and need for complex data processing have remained limiting factors in uptake outside of what remains predominantly large research institutions. Progress and advances in deep neural networks, trained on millions of clinically labelled datasets, have allowed the development of a computer vision system which enables assessment using a handheld smartphone with no markers and accurate range of motion for knee during flexion and extension. This allows clinicians and therapists to objectively track progress without the need for complex and expensive equipment or time-consuming analysis, which was concluded to be lacking during a recent systematic review of existing applications. Method. A smartphone based computer vision system was assessed for accuracy with a gold standard comparison using a validated ‘traditional’ infra-red motion capture system which had a defined calibrated accuracy of 0.1degrees. A total of 22 subjects were assessed simultaneously using both the computer vision smartphone application and the standard motion capture system. Assessment of the handheld system was made by comparison to the motion capture system for knee flexion and extension angles through a range of motion with a simulated fixed-flexion deformity which prevented full extension to assess the accuracy of the system, repeating movements ten times. The peak extension angles and also numerous discrete angle measurements were compared between the two systems. Repeatability was assessed by comparing several sequential cycles of flexion/extension and comparison of the maximum range of motion in normal knees and in those with a simulated fixed-flexion deformity. In addition, discrete angles were also measured on both legs of three cadavers with both skin and then bone implanted fiducial markers for ground truth reliability accounting for skin movement. Data was processed quickly through an automated secure cloud system. Results. The smartphone application was found to be accurate to 1.47±1.05 degrees through a full range of motion and 1.75±1.56 degrees when only peak extension angles were compared, demonstrating excellent reliability and repeatability. The cadaveric studies despite limitations which will be discussed still showed excellent accuracy with average errors as low as 0.29 degrees for individual angles and 4.09 degrees for an average error in several measurement. Conclusion. This novel solution offers for the first time a way to objectively measure knee range of motion using a markerless handheld device and enables tracking through a range of assessments with proven accuracy and reliability even accounting for traditional issues with the previous marker based systems. Repeatability for both computer vision and motion capture have greater extrinsic than intrinsic error, particularly with marker placement - another benefit of a markerless system. Clinical applications include pre-operative assessment and post-operative follow-up, paired with surgical planning (including with robots) and
Introduction. Proper total knee arthroplasty balancing relies on accurate component positioning and alignment as well as soft tissue tensioning. Technology for cutting guide alignment has evolved from the “free hand” technique in the 1970's, to traditional intra/extra medullary rods in the 1980's and 1990's, to computer navigated surgery in the 2000's, and finally to patient specific custom cutting blocks in the 2010's. The latest technique is a modification to conventional computer navigation assisted surgery using Brainlab's Dash™ TKA/THA software platform that runs as an application on an Apple IPod held by the surgeon in a sterile pouch in the operative field. The handheld IPod touch screen allows the surgeon to control all aspects of the navigation interface without needing the assistance of an observer to manually run the software. In addition, the surgeon is able to always focus on the operative field while ‘navigating’ without looking up at a
