µCT images are commonly analysed to assess changes in bone density and architecture in preclinical murine models. Several platforms provide automated analysis of bone architecture parameters from volumetric regions of interest (ROI). However, segmentation of the regions of subchondral bone to create the volumetric ROIs remains a manual and time-consuming task. This study aimed to develop and evaluate automated pipelines for trabecular bone architecture analysis of mouse proximal tibia subchondral bone.

A segmented dataset involving 62 knees (healthy and arthritic) from 10-week male C57BL/6 mice were used to train a U-Net type architecture, with µCT scans (downsampled) input that output segmentation and bone volume density (BV/TV) of the subchondral trabecular bone. Segmentations were upsampled and used in tandem with the original scans (10µ) as input for architecture analysis along with the thresholded trabecular bone. The analysis considered the manually and U-Net segmented ROIs using two available pipelines: the ITKBoneMorphometry library and CTan (SKYSCAN). The analyses included: bone volume (BV), total volume (TV), BV/TV, trabecular number (TbN), trabecular thickness (TbTh), trabecular separation (TbSp), and bone surface density (BSBV).

There was good agreement for bone measures between the manual and U-Net pipelines utilizing ITK (R=0.88-0.98) and CTan (R=0.91-0.98). ITK and CTan showed good agreement for BV, TV, BV/TV, TbTh and BSBV (R=0.9-0.98). However, a limited agreement was seen between TbN (R=0.73) and TbSb (R=0.59) due to methodological differences in how spacing is evaluated.

This U-Net/ITK pipeline seamlessly automated both segmentation and quantification of the proximal tibia subchondral bone. This automated pipeline allows the analysis of large volumes of data, and its open-source nature may enable the standardization of stereologic analysis of trabecular bone across different research groups.

Physiotherapy is a critical element in successful conservative management of low back pain (LBP). The aim of this study was to develop and evaluate a system with wearable inertial sensors to objectively detect sitting postures and performance of unsupervised exercises containing movement in multiple planes (flexion, extension, rotation).

A set of 8 inertial sensors were placed on 19 healthy adult subjects. Data was acquired as they performed 7 McKenzie low-back exercises and 3 sitting posture positions. This data was used to train two models (Random Forest (RF) and XGBoost (XGB)) using engineered time series features. In addition, a convolutional neural network (CNN) was trained directly on the time series data. A feature importance analysis was performed to identify sensor locations and channels that contributed most to the models. Finally, a subset of sensor locations and channels was included in a hyperparameter grid search to identify the optimal sensor configuration and the best performing algorithm(s) for exercise classification. Models were evaluated using F1-score in a 10-fold cross validation approach.

The optimal hardware configuration was identified as a 3-sensor setup using lower back, left thigh, and right ankle sensors with acceleration, gyroscope, and magnetometer channels. The XBG model achieved the highest exercise (F1=0.94±0.03) and posture (F1=0.90±0.11) classification scores. The CNN achieved similar results with the same sensor locations, using only the accelerometer and gyroscope channels for exercise classification (F1=0.94±0.02) and the accelerometer channel alone for posture classification (F1=0.91±0.03).

This study demonstrates the potential of a 3-sensor lower body wearable solution (e.g. smart pants) that can identify proper sitting postures and exercises in multiple planes, suitable for low back pain. This technology has the potential to improve the effectiveness of LBP rehabilitation by facilitating quantitative feedback, early problem diagnosis, and possible remote monitoring.

Bone turnover and microdamage are impacted by skeletal metastases which can contribute to increased fracture risk. Treatments for metastatic disease may further impact bone quality. This study aimed to establish an understanding of microdamage accumulation and load to failure in healthy and osteolytic vertebrae following cancer treatment (stereotactic body radiotherapy (SBRT), zoledronic acid (ZA), or docetaxel (DTX)).

Forty-two 6-week old athymic female rats (Hsd:RH-Foxn1rnu, Envigo) were studied; 22 were inoculated with HeLa cervical cancer cells through intracardiac injection (day 0). Animals were randomly assigned to four groups: untreated (healthy=5, osteolytic=6), SBRT on day 14 (healthy=6, osteolytic=6), ZA on day 7 (healthy=4, osteolytic=5), and DTX on day 14 (healthy=5, osteolytic=5). Animals were euthanized on day 21. L1-L3 motion segments were compression loaded to failure and force-displacement data recorded. T13 vertebrae were stained with BaSO₄ and µCT imaged (90kVp, 44uA, 4.9µm) to visualize microdamage location and volume. Damage volume fraction (DV/BV) was calculated as the ratio of BaSO₄ to bone volume. Differences in mean load-to-failure were compared using three-way ANOVA (disease status, treatment, cells injected). Differences in mean DV/BV between treatment groups were compared using one-way ANOVA.

Treatment had a significant effect on load-to-failure (p=0.004) with ZA strengthening the healthy and osteolytic vertebrae. Reduced strength post SBRT seen in the metastatic (but not the healthy) group may be explained by greater tumor involvement secondary to higher cell injection concentrations. Untreated metastatic samples had higher DV/BV (16.25±2.54%) compared to all treatment groups (p<0.05) suggesting a benefit of treatment to bone quality.

Focal and systemic cancer treatments were shown to effect load-to-failure and microdamage accumulation in healthy and osteolytic vertebrae. Developing a better understanding of how treatments effect bone quality and mechanical stability is critical for effective management of patients with spinal metastases.

Access to health care, including physiotherapy, is increasingly occurring through virtual formats. At-home adherence to physical therapy programs is often poor and few tools exist to objectively measure low back physiotherapy exercise participation without the direct supervision of a medical professional. The aim of this study was to develop and evaluate the potential for performing automatic, unsupervised video-based monitoring of at-home low back physiotherapy exercises using a single mobile phone camera.

24 healthy adult subjects performed seven exercises based on the McKenzie low back physiotherapy program while being filmed with two smartphone cameras. Joint locations were automatically extracted using an open-source pose estimation framework. Engineered features were extracted from the joint location time series and used to train a support vector machine classifier (SVC). A convolutional neural network (CNN) was trained directly on the joint location time series data to classify exercises based on a recording from a single camera. The models were evaluated using a 5-fold cross validation approach, stratified by subject, with the class-balanced accuracy used as the performance metric.

Optimal performance was achieved when using a total of 12 pose estimation landmarks from the upper and lower body, with the SVC model achieving a classification accuracy of 96±4% and the CNN model an accuracy of 97±2%.

This study demonstrates the feasibility of using a smartphone camera and a supervised machine learning model to effectively assess at-home low back physiotherapy adherence. This approach could provide a low-cost, scalable method for tracking adherence to physical therapy exercise programs in a variety of settings.