Gait measurements can vary due to various intrinsic and extrinsic factors, and this variability becomes more pronounced using inertial sensors in a free-living environment. Therefore, identifying and quantifying the sources of variability is essential to ensure measurement reliability and maintain data quality.

This study aimed to determine the variability of daily accelerations recorded by an inertial sensor in a group of healthy individuals. Ten participants, four males and six females, with a mean age of 50 years (range: 29–61) and BMI of 26.9 kg/m² (range: 21.4–36.8), were included. A single accelerometer continuously recorded lower limb accelerations over two weeks. We extracted and analyzed the accelerations of three consecutive strides within walking bouts if the time difference between the bouts was more than two hours. Multivariate mixed-effects modeling was performed on both the discretized acceleration waveforms at 101 points (0–100) and the harmonics of the signals in the frequency domain to determine the variance components for different subjects, days, bouts, and steps as the random effect variables. Intraclass correlation coefficients (ICCs) were calculated for between-day, between-bout, and between-step comparisons.

The results showed that the ICCs for the between-day, between-bout, and between-step comparisons were 0.73, 0.82, 0.99 for the vertical axis; 0.64, 0.75, 0.99 for the anteroposterior axis; and 0.55, 0.96, 0.97 for the mediolateral axis. For the signal harmonics, the respective ICCs were 0.98, 0.98, 0.99 for the vertical axis; 0.54, 0.93, 0.98 for the anteroposterior axis; and 0.69, 0.78, 0.95 for the mediolateral axis.

Overall, this study demonstrated that accelerations recorded continuously for multiple days in a free-living environment exhibit high variability, mainly between days, and some variability arising from differences between walking bouts during different times within days. However, reliable and repeatable gait measurements can be obtained by identifying and quantifying the sources of variability.

Smartphones are often equipped with inertial sensors capable of measuring individuals' physical activities. Their role in monitoring the patients' physical activities in telemedicine, however, needs to be explored. The main objective of this study was to explore the correlation between a participant's daily step counts and the daily step counts reported by their smartphone. This prospective observational study was conducted on patients undergoing lower limb orthopedic surgery and a group of non-patients. The data collection period was from 2 weeks before until four weeks after the surgery for the patients and two weeks for the non-patients. The participants' daily steps were recorded by physical activity trackers employed 24/7, and an application recorded the number of daily steps registered by the participants' smartphones. We compared the cross-correlation between the daily steps time-series taken from the smartphones and physical activity trackers in different groups of participants. We also employed mixed modeling to estimate the total number of steps. Overall, 1067 days of data were collected from 21 patients (11 females) and 10 non-patients (6 females). The cross-correlation coefficient between the smartphone and physical activity tracker was 0.70 [0.53–0.83]. The correlation in the non-patients was slightly higher than in the patients (0.74 [0.60–0.90] and 0.69 [0.52–0.81], respectively). Considering the ubiquity, convenience, and practicality of smartphones, the high correlation between the smartphones and the total daily step time-series highlights the potential usefulness of smartphones in detecting the change in the step counts in remote monitoring of the patient's physical activity.

The most important outcome predictor of Legg-Calvé-Perthes disease (LCPD) is the shape of the healed femoral head. However, the deformity of the femoral head is currently evaluated by non-reproducible, categorical, and qualitative classifications. In this regard, recent advances in computer vision might provide the opportunity to automatically detect and delineate the outlines of bone in radiographic images for calculating a continuous measure of femoral head deformity. This study aimed to construct a pipeline for accurately detecting and delineating the proximal femur in radiographs of LCPD patients employing existing algorithms. To detect the proximal femur, the pretrained stateof-the-art object detection model, YOLOv5, was trained on 1580 manually annotated radiographs, validated on 338 radiographs, and tested on 338 radiographs. Additionally, 200 radiographs of shoulders and chests were added to the dataset to make the model more robust to false positives and increase generalizability. The convolutional neural network architecture, U-Net, was then employed to segment the detected proximal femur. The network was trained on 80 manually annotated radiographs using real-time data augmentation to increase the number of training images and enhance the generalizability of the segmentation model. The network was validated on 60 radiographs and tested on 60 radiographs. The object detection model achieved a mean Average Precision (mAP) of 0.998 using an Intersection over Union (IoU) threshold of 0.5, and a mAP of 0.712 over IoU thresholds of 0.5 to 0.95 on the test set. The segmentation model achieved an accuracy score of 0.912, a Dice Coefficient of 0.937, and a binary IoU score of 0.854 on the test set. The proposed fully automatic proximal femur detection and segmentation system provides a promising method for accurately detecting and delineating the proximal femoral bone contour in radiographic images, which is necessary for further image analysis.

Wearable inertial sensors can detect abnormal gait associated with knee or hip osteoarthritis (OA). However, few studies have compared sensor-derived gait parameters between patients with hip and knee OA or evaluated the efficacy of sensors suitable for remote monitoring in distinguishing between the two. Hence, our study seeks to examine the differences in accelerations captured by low-frequency wearable sensors in patients with knee and hip OA and classify their gait patterns.

We included patients with unilateral hip and knee OA. Gait analysis was conducted using an accelerometer ipsilateral with the affected joint on the lateral distal thighs. Statistical parametric mapping (SPM) was used to compare acceleration signals. The k-Nearest Neighbor (k-NN) algorithm was trained on 80% of the signals' Fourier coefficients and validated on the remaining 20% using 10-fold cross-validation to classify the gait patterns into hip and knee OA.

We included 42 hip OA patients (19 females, age 70 [63–78], BMI of 28.3 [24.8–30.9]) and 59 knee OA patients (31 females, age 68 [62–74], BMI of 29.7 [26.3–32.6]). The SPM results indicated that one cluster (12–20%) along the vertical axis had accelerations exceeding the critical threshold of 2.956 (p=0.024). For the anteroposterior axis, three clusters were observed exceeding the threshold of 3.031 at 5–19% (p = 0.0001), 39–54% (p=0.00005), and 88–96% (p = 0.01). Regarding the mediolateral axis, four clusters were identified exceeding the threshold of 2.875 at 0–9% (p = 0.02), 14–20% (p=0.04), 28–68% (p < 0.00001), and 84–100% (p = 0.004). The k-NN model achieved an AUC of 0.79, an accuracy of 80%, and a precision of 85%.

In conclusion, the Fourier coefficients of the signals recorded by wearable sensors can effectively discriminate the gait patterns of knee and hip OA. In addition, the most remarkable differences in the time domain were observed along the mediolateral axis.

The tendency towards using inertial sensors for remote monitoring of the patients at home is increasing. One of the most important characteristics of the sensors is sampling rate. Higher sampling rate results in higher resolution of the sampled signal and lower amount of noise. However, higher sampling frequency comes with a cost. The main aim of our study was to determine the validity of measurements performed by low sampling frequency (12.5 Hz) accelerometers (SENS) in patients with knee osteoarthritis compared to standard sensor-based motion capture system (Xsens). We also determined the test-retest reliability of SENS accelerometers.

Participants were patients with unilateral knee osteoarthritis. Gait analysis was performed simultaneously by using Xsens and SENS sensors during two repetitions of over-ground walking at a self-selected speed. Gait data from Xsens were used as an input for AnyBody musculoskeletal modeling software to measure the accelerations at the exact location of two defined virtual sensors in the model (VirtualSENS). After preprocessing, the signals from SENS and VirtualSENS were compared in different coordinate axes in time and frequency domains. ICC for SENS data from first and second trials were calculated to assess the repeatability of the measurements.

We included 32 patients (18 females) with median age 70.1[48.1 – 85.4]. Mean height and weight of the patients were 173.2 ± 9.6 cm and 84.2 ± 14.7 kg respectively. The correlation between accelerations in time domain measured by SENS and VirtualSENS in different axes was r = 0.94 in y-axis (anteroposterior), r = 0.91 in x-axis (vertical), r = 0.83 in z-axis (mediolateral), and r = 0.89 for the magnitude vector. In frequency domain, the value and the power of fundamental frequencies (F₀) of SENS and VirtualSENS signals demonstrated strong correlation (r = 0.98 and r = 0.99 respectively). The result of test-retest evaluation showed excellent repeatability for acceleration measurement by SENS sensors. ICC was between 0.89 to 0.94 for different coordinate axes.

Low sampling frequency accelerometers can provide valid and reliable measurements especially for home monitoring of the patients, in which handling big data and sensors cost and battery lifetime are among important issues.

An international Consensus Group has by a Delphi approach identified the topic of host factors affecting pin site infection to be one of the top 10 priorities in external fixator management. The aim of this study was to report the frequency of studies reporting on specific host factors as a significant association with pin site infection. Host factors to be assessed was: age, smoking, BMI and any comorbidity, diabetes, in particular. The intention was an ethological review, data was extracted if feasible, however no meta-analysis was performed.

A systematic literature search was performed according to the PRISMA-guidelines. The protocol was registered before data extraction in PROSPERO. The search string was based on the PICO criterias. A logic grid with key concept and index terms was made. A search string was built assisted by a librarian. The literature search was executed in three electronic bibliographic databases, including Embase MEDLINE (1111 hits) and CINAHL (2066 hits) via Ovid and Cochrane Library CENTRAL (387 hits). Inclusion criteria: external fixation, >1 pin site infection, host factor of interest, peer-reviewed journal. Exclusion criteria: Not written in English, German, Danish, Swedish, or Norwegian, animal or cadaveric studies, location on head, neck, spine, cranium or thorax, editorials or conference abstract. The screening process was done using Covidence.

A total of 3564 titles found. 3162 excluded by title and abstract screening. 140 assessed for full text eligibility. 11 studies included for data extraction. The included studies all had a retrospective design. Three identified as case-control studies. Generally the included studies was assessed to have a high risk of bias. A significant associations between pin site infection for following host factors: a) increased HbA1C level in diabetic patients; b) congestive heart failure in diabetic patients; c) less co-morbidity; d) preoperative osteomyelitis was found individually.

This systematic literature search identified a surprisingly low number of studies examining for risk of pin site infection and host factors. Thus, this review most of all serves to demonstrate a gap of evidence about correlation between host factors and risk of pin site infection, and further studies are warranted.

There is no consensus on how to evaluate and grade pin site infection. A precise, objective and reliable pin site infectious score is warranted. The literature was reviewed for pin site infection classification systems, The Modified Gordon Score (MGS) grade 0-6 was used. The aim was to test the reliability of The Modified Gordon Infection Score. The observed agreement and inter-rater reliability were investigated between nurse and doctors.

MGS was performed in the outpatient clinic at Aalborg University Hospital, Denmark on 1472 pin sites in 119 patients by one nurse and one of three orthopaedic surgeons blinded to each other's judgement. The data was stored in a Red Cap Database for further statistical analysis. The observed agreement between the nurse and the 3 orthopaedic surgeons was evaluated with a one-way random-effect model with interclass correlation with absolute agreement. Furthermore the observed agreement for each of the 3 surgeons with the nurse was calculated.

The distribution of MGS infection grade in the 1472 pin sites was: Grade 0; n=1372, Grade 1; n=32, Grade 2; n=39, Grade 3; n=24, Grade 4; n=5, Grade 5; n=0, Grade 6; n=0.

The observed agreement between the nurse and the surgeons was calculated as 98%. The ICC estimated between nurse and the surgeons was 0,8943 (ICC >0,85 = reliable). The grading was done by three different doctors with an agreement with the nurse as follows. Rater1 (n=416) =99,5 %, Rater2 (n=1440) =97,4%, Rater3 (n=1440) =96,6%.

A limitation to this study is that the dataset represents mostly clean pin sites with MGS 0. Only 100 pin sites had signs of superficial infection MGS 1-4 none above 4. We found that the MGS infection score is highly reliable for low grade infections but we cannot conclude on reliability in severe infections.

To detect early signs of infection infrared thermography has been suggested to provide quantitative information. Our vision is to invent a pin site infection thermographic surveillance tool for patients at home. A preliminary step to this goal is the aim of this study, to automate the process of locating the pin and detecting the pin sites in thermal images efficiently, exactly, and reliably for extracting pin site temperatures.

A total of 1708 pin sites was investigated with Thermography and augmented by 9 different methods in to totally 10.409 images. The dataset was divided into a training set (n=8325), a validation set (n=1040), and a test set (n=1044) of images. The Pin Detection Model (PDM) was developed as follows: A You Only Look Once (YOLOv5) based object detection model with a Complete Detection Intersection over Union (CDIoU), it was pre-trained and finetuned by the through transfer learning. The basic performance of the YOLOv5 with CDIoU model was compared with other conventional models (FCOS and YOLOv4) for deep and transition learning to improve performance and precision. Maximum Temperature Extraction (MTE) Based on Region of Interest (ROI) for all pin sites was generated by the model. Inference of MTE using PDM with infected and un-infected datasets was investigated.

An automatic tool that can identify and annotate pin sites on conventional images using bounding boxes was established. The bounding box was transferred to the infrared image. The PMD algorithm was built on YOLOv5 with CDIoU and has a precision of 0.976. The model offers the pin site detection in 1.8 milliseconds. The thermal data from ROI at the pin site was automatically extracted.

These results enable automatic pin site annotation on thermography. The model tracks the correlation between temperature and infection from the detected pin sites and demonstrates it is a promising tool for automatic pin site detection and maximum temperature extraction for further infection studies. Our work for automatic pin site annotation on thermography paves the way for future research on infection assessment using thermography.

Introduction and Objective

Several studies have described double and triple femoral neck lengthening osteotomies to correct coxa brevis deformity, however, no overview exists in literature. Our aim was to perform the first systematic review of the outcomes of double and triple femoral neck lengthening.

Introduction and Objective

Digital infra-red thermography may have the capability of identifying local inflammations. Nevertheless, the role of thermography in diagnosing pin site infection has not been explored yet and the reliability and validity of this method for pin site surveillance is in question. The purpose of this study was to explore the capability and intra-rater reliability of thermography in detecting pin site infection.

Introduction and Objective

Continuous peripheral nerve blocks (cPNBs) have shown good results in pain management after orthopedic surgeries. However, the variation of performance between different subspecialities is unknown. The aim of this study is to describe our experience with cPNBs after lower limb orthopedic surgeries in different subspecialties.

In 2019, Lin et al. published a proof-of-concept study of electrical impedance spectroscopy as a simple and low-cost method to characterize progression of fracture repair (Lin et al., Sci Rep 2019). However, the electrical impedance sensors were placed in the fracture site which may impair the transfer to clinical use. To further explore the concept of monitoring fracture healing by electrical impedance spectroscopy, we established a tibial fracture model in the rabbit where sensors are positioned in proximity to the fracture site but without being placed in the fracture site. The aim of this pilot study was to explore whether distinct patterns of electrical impedance would evolve as tibial fractures in rabbits were evaluated until radiographic signs of healing.

Approval was granted from the Inspectorate of the Animal Experimentation under the Danish Ministry of Justice. Four rabbits were anaesthetized, and in each rabbit a tibial osteotomy was made and stabilized by an external fixator. Electrical impedance was measured immediately postoperative and hereafter daily until euthanization after 3 weeks. Recordings were obtained within a wide frequency range (10 Hz to 1 MHz) from an inner electrode placed into the medullary canal and an outer electrode placed extracortical on the lateral with a distance of 3 mm to the defect.

A similar pattern of electrical impedance over time was observed in the four rabbits. During the very early stages of fracture healing, an initial fluctuation in electrical impedance occurred. However, after 10 days the curves revealed a steady daily increase in electrical impedance. The first radiological signs of bone healing were detected after 14 days and progressed in all four rabbits in accordance with increments in the electrical impedance until termination of the pilot study after 21 days.

Consistent electrical impedance patterns were detected during bone healing in a pilot study of four rabbits. Further research is needed to explore whether the presented method of electrical impedance measurements can be used to monitor bone healing over time.