Knee swelling is common after injury or surgery, resulting in pain, restricted range of movement and limited mobility. Accurately measuring knee swelling is critical to assess recovery. However, current measurement methods are either unreliable or expensive [1,2]. Therefore, a new measurement method is developed. This wearable (the ‘smart brace’) has shown the ability to distinguish a swollen knee from a not swollen knee using multi-frequency-bio impedance analysis (MF-BIA) [3].

This study aimed to determine the accuracy of this smart brace. The study involved 25 usable measurements on patients treated for unilateral knee osteoartritis with a 5mL injection of Lidocaïne + DepoMedrol (1:4). MF-BIA measurements were taken before and after the injection, both on the treated and untreated knee. The smart brace accurately measured the effect of the injection by a decrease in resistance of up to 2.6% at 100kHz (p<0.01), where commonly used gel electrodes were unable to measure the relative difference. Remarkably, both the smart brace and gel electrodes showed a time component in the MF-BIA measurements.

To further investigate this time component, 10 participants were asked to lie down for 30 minutes, with measurements taken every 3 minutes using both gel electrodes and the smart brace on both legs. The relative change between each time step was calculated to determine changes over time. The results showed presence of a physiological aspect (settling of knee fluids), and for the brace also a mechanical aspect (skin-electrode interface) [4]. The mechanical aspect mainly interfered with reactance values.

Overall, the smart brace is a feasible method for quantitatively measuring knee swelling as a relative change over time. However, the skin-electrode interface should be improved for reliable measurements at different moments in time. The findings suggest that the smart brace could be a promising tool for monitoring knee swelling during rehabilitation.

Artificial Intelligence (AI) is becoming more powerful but is barely used to counter the growth in health care burden. AI applications to increase efficiency in orthopedics are rare. We questioned if (1) we could train machine learning (ML) algorithms, based on answers from digitalized history taking questionnaires, to predict treatment of hip osteoartritis (either conservative or surgical); (2) such an algorithm could streamline clinical consultation.

Multiple ML models were trained on 600 annotated (80% training, 20% test) digital history taking questionnaires, acquired before consultation. Best performing models, based on balanced accuracy and optimized automated hyperparameter tuning, were build into our daily clinical orthopedic practice. Fifty patients with hip complaints (>45 years) were prospectively predicted and planned (partly blinded, partly unblinded) for consultation with the physician assistant (conservative) or orthopedic surgeon (operative). Tailored patient information based on the prediction was automatically sent to a smartphone app. Level of evidence: IV.

Random Forest and BernoulliNB were the most accurate ML models (0.75 balanced accuracy). Treatment prediction was correct in 45 out of 50 consultations (90%), p<0.0001 (sign and binomial test). Specialized consultations where conservatively predicted patients were seen by the physician assistant and surgical patients by the orthopedic surgeon were highly appreciated and effective.

Treatment strategy of hip osteoartritis based on answers from digital history taking questionnaires was accurately predicted before patients entered the hospital. This can make outpatient consultation scheduling more efficient and tailor pre-consultation patient education.

3D measurement of joint angles so far has only been possible using marker-based movement analysis, and therefore has not been applied in (larger scale) clinical practice (performance test) and even less so in the free field (activity monitoring). 3D joint angles could provide useful additional information in assessing the risk of anterior cruciate ligament injury using a vertical drop jump or in assessing knee range of motion after total knee arthroplasty. We developed a tool to measure dynamic 3D joint angles using 6 inertial sensors, attached to left and right shank, thigh and pelvis. The same sensors have been used for activity identification in a previous study. To validate the setup in a pilot study, we measured 3D knee and hip angles using the sensors and a Vicon movement lab simultaneously in 3 subjects. Subjects performed drop jumps, squats and ran on the spot. The mean error between Vicon and sensor measurement for the maximum joint angles was 3, 7 and 8 degrees for knee flexion, ad/abduction and rotation respectively, and 9, 7 and 10 degrees for hip flexion, ad/abduction and rotation respectively. No calibration movements were required. A major part of the inaccuracy was caused by soft tissue effects and can partly be resolved by improved sensor attachment. These pilot results show that it is feasible to measure 3D joint angles continuously using unobtrusive light-weight sensors. No movement lab is necessary and therefore the measurements can be done in a free field setting, e.g. at home or during training at a sport club. A more extensive validation study will be performed in the near future.