Effects of a progressive aquatic resistance exercise program on the biochemical composition and morphology of cartilage in women with mild knee osteoarthritis: protocol for a randomised controlled trial

Peer reviewe

Temporal Trends in Vertebral Size and Shape from Medieval to Modern-Day

Human lumbar vertebrae support the weight of the upper body. Loads lifted and carried by the upper extremities cause significant loading stress to the vertebral bodies. It is well established that trauma-induced vertebral fractures are common especially among elderly people. The aim of this study was to investigate the morphological factors that could have affected the prevalence of trauma-related vertebral fractures from medieval times to the present day. To determine if morphological differences existed in the size and shape of the vertebral body between medieval times and the present day, the vertebral body size and shape was measured from the 4th lumbar vertebra using magnetic resonance imaging (MRI) and standard osteometric calipers. The modern samples consisted of modern Finns and the medieval samples were from archaeological collections in Sweden and Britain. The results show that the shape and size of the 4th lumbar vertebra has changed significantly from medieval times in a way that markedly affects the biomechanical characteristics of the lumbar vertebral column. These changes may have influenced the incidence of trauma- induced spinal fractures in modern populations

Machine learning based texture analysis of patella from X-rays for detecting patellofemoral osteoarthritis

Abstract Objective: To assess the ability of texture features for detecting radiographic patellofemoral osteoarthritis (PFOA) from knee lateral view radiographs. Design: We used lateral view knee radiographs from The Multicenter Osteoarthritis Study (MOST) public use datasets (n = 5507 knees). Patellar region-of-interest (ROI) was automatically detected using landmark detection tool (BoneFinder), and subsequently, these anatomical landmarks were used to extract three different texture ROIs. Hand-crafted features, based on Local Binary Patterns (LBP), were then extracted to describe the patellar texture. First, a machine learning model (Gradient Boosting Machine) was trained to detect radiographic PFOA from the LBP features. Furthermore, we used end-to-end trained deep convolutional neural networks (CNNs) directly on the texture patches for detecting the PFOA. The proposed classification models were eventually compared with more conventional reference models that use clinical assessments and participant characteristics such as age, sex, body mass index (BMI), the total Western Ontario and McMaster Universities Arthritis Index (WOMAC) score, and tibiofemoral Kellgren-Lawrence (KL) grade. Atlas-guided visual assessment of PFOA status by expert readers provided in the MOST public use datasets was used as a classification outcome for the models. Performance of prediction models was assessed using the area under the receiver operating characteristic curve (ROC AUC), the area under the precision-recall (PR) curve -average precision (AP)-, and Brier score in the stratified 5-fold cross validation setting. Results: Of the 5507 knees, 953 (17.3%) had PFOA. AUC and AP for the strongest reference model including age, sex, BMI, WOMAC score, and tibiofemoral KL grade to predict PFOA were 0.817 and 0.487, respectively. Textural ROI classification using CNN significantly improved the prediction performance (ROC AUC = 0.889, AP = 0.714). Conclusions: We present the first study that analyses patellar bone texture for diagnosing PFOA. Our results demonstrates the potential of using texture features of patella to predict PFOA

Predicting knee osteoarthritis progression from structural MRI using deep learning

Abstract Accurate prediction of knee osteoarthritis (KOA) progression from structural MRI has a potential to enhance disease understanding and support clinical trials. Prior art focused on manually designed imaging biomarkers, which may not fully exploit all disease-related information present in MRI scan. In contrast, our method learns relevant representations from raw data end-to-end using Deep Learning, and uses them for progression prediction. The method employs a 2D CNN to process the data slice-wise and aggregate the extracted features using a Transformer. Evaluated on a large cohort (n=4,866), the proposed method outperforms conventional 2D and 3D CNN-based models and achieves average precision of 0.58 ± 0.03 and ROC AUC of 0.78 ± 0.01. This paper sets a baseline on end-to-end KOA progression prediction from structural MRI. Our code is publicly available at https://github.com/MIPT-Oulu/OAProgressionMR

Physical properties of cartilage by relaxation anisotropy

Abstract Articular cartilage exhibits complex structural and compositional anisotropy. The anisotropy and arrangement of the collagen network, concentration of proteoglycan aggregates and water content vary as functions of cartilage depth in healthy, adult cartilage. Furthermore, these tissue properties are altered in cartilage maturation and degeneration alike. Relaxation anisotropy occurs widely in cartilage tissue and manifests itself directly in different relaxation parameters. This chapter summarizes the association between anisotropic physical properties of cartilage and the most important relaxation parameters

Effectiveness of digital counseling environments on anxiety, depression, and adherence to treatment among patients who are chronically ill:systematic review

Abstract Background: Patients who are chronically ill need novel patient counseling methods to support their self-care at different stages of the disease. At present, knowledge of how effective digital counseling is at managing patients’ anxiety, depression, and adherence to treatment seems to be fragmented, and the development of digital counseling will require a more comprehensive view of this subset of interventions. Objective: This study aims to identify and synthesize the best available evidence on the effectiveness of digital counseling environments at improving anxiety, depression, and adherence to treatment among patients who are chronically ill. Methods: Systematic searches of the EBSCO (CINAHL), PubMed, Scopus, and Web of Science databases were conducted in May 2019 and complemented in October 2020. The review considered studies that included adult patients aged ≥18 years with chronic diseases; interventions evaluating digital (mobile, web-based, and ubiquitous) counseling interventions; and anxiety, depression, and adherence to treatment, including clinical indicators related to adherence to treatment, as outcomes. Methodological quality was assessed using the standardized Joanna Briggs Institute critical appraisal tool for randomized controlled trials or quasi-experimental studies. As a meta-analysis could not be conducted because of considerable heterogeneity in the reported outcomes, narrative synthesis was used to synthesize the results. Results: Of the 2056 records screened, 20 (0.97%) randomized controlled trials, 4 (0.19%) pilot randomized controlled trials, and 2 (0.09%) quasi-experimental studies were included. Among the 26 included studies, 10 (38%) digital, web-based interventions yielded significantly positive effects on anxiety, depression, adherence to treatment, and the clinical indicators related to adherence to treatment, and another 18 (69%) studies reported positive, albeit statistically nonsignificant, changes among patients who were chronically ill. The results indicate that an effective digital counseling environment comprises high-quality educational materials that are enriched with multimedia elements and activities that engage the participant in self-care. Because of the methodological heterogeneity of the included studies, it is impossible to determine which type of digital intervention is the most effective for managing anxiety, depression, and adherence to treatment. Conclusions: This study provides compelling evidence that digital, web-based counseling environments for patients who are chronically ill are more effective than, or at least comparable to, standard counseling methods; this suggests that digital environments could complement standard counseling

Improving robustness of deep learning based knee MRI segmentation:mixup and adversarial domain adaptation

Abstract Degeneration of articular cartilage (AC) is actively studied in knee osteoarthritis (OA) research via magnetic resonance imaging (MRI). Segmentation of AC tissues from MRI data is an essential step in quantification of their damage. Deep learning (DL) based methods have shown potential in this realm and are the current state-of-the-art, however, their robustness to heterogeneity of MRI acquisition settings remains an open problem. In this study, we investigated two modern regularization techniques — mixup and adversarial unsupervised domain adaptation (UDA) — to improve the robustness of DL-based knee cartilage segmentation to new MRI acquisition settings. Our validation setup included two datasets produced by different MRI scanners and using distinct data acquisition protocols. We assessed the robustness of automatic segmentation by comparing mixup and UDA approaches to a strong baseline method at different OA severity stages and, additionally, in relation to anatomical locations. Our results showed that for moderate changes in knee MRI data acquisition settings both approaches may provide notable improvements in the robustness, which are consistent for all stages of the disease and affect the clinically important areas of the knee joint. However, mixup may be considered as a recommended approach, since it is more computationally efficient and does not require additional data from the target acquisition setup

Diffusion and near-equilibrium distribution of MRI and CT contrast agents in articular cartilage

Charged contrast agents have been used both in vitro and in vivo for estimation of the fixed charge density (FCD) in articular cartilage. In the present study, the effects of molecular size and charge on the diffusion and equilibrium distribution of several magnetic resonance imaging (MRI) and computed tomography (CT) contrast agents were investigated. Full thickness cartilage disks ( = 4.0 mm, n = 64) were prepared from fresh bovine patellae. Contrast agent (gadopentetate: Magnevist, gadodiamide: Omniscan™, ioxaglate: Hexabrix™ or sodium iodide: NaI) diffusion was allowed either through the articular surface or through the deep cartilage. CT imaging of the samples was conducted before contrast agent administration and after 1, 5, 9, 16, 25 and 29 h (and with three samples after 2, 3, 4 and 5 days) diffusion using a clinical peripheral quantitative computed tomography (pQCT) instrument. With all contrast agents, the diffusion through the deep cartilage was slower when compared to the diffusion through the articular surface. With ioxaglate, gadopentetate and gadodiamide it took over 29 h for diffusion to reach the near-equilibrium state. The slow diffusion of the contrast agents raise concerns regarding the validity of techniques for FCD estimation, as these contrast agents may not reach the equilibrium state that is assumed. However, since cartilage composition, i.e. deep versus superficial, had a significant effect on diffusion, imaging of the nonequilibrium diffusion process might enable more accurate assessment of cartilage integrity

Deep learning-based segmentation of knee MRI for fully automatic subregional morphological assessment of cartilage tissues:data from the Osteoarthritis Initiative

Abstract Morphological changes in knee cartilage subregions are valuable imaging-based biomarkers for understanding progression of osteoarthritis, and they are typically detected from magnetic resonance imaging (MRI). So far, accurate segmentation of cartilage has been done manually. Deep learning approaches show high promise in automating the task; however, they lack clinically relevant evaluation. We introduce a fully automatic method for segmentation and subregional assessment of articular cartilage, and evaluate its predictive power in context of radiographic osteoarthritis progression. Two data sets of 3D double-echo steady-state (DESS) MRI derived from the Osteoarthritis Initiative were used: first, n = 88; second, n = 600, 0-/12-/24-month visits. Our method performed deep learning-based segmentation of knee cartilage tissues, their subregional division via multi-atlas registration, and extraction of subregional volume and thickness. The segmentation model was developed and assessed on the first data set. Subsequently, on the second data set, the morphological measurements from our and the prior methods were analyzed in correlation and agreement, and, eventually, by their discriminative power of radiographic osteoarthritis progression over 12 and 24 months, retrospectively. The segmentation model showed very high correlation (r > 0.934) and agreement (mean difference <  116 mm³) in volumetric measurements with the reference segmentations. Comparison of our and manual segmentation methods yielded r = 0.845–0.973 and mean differences = 262–501 mm³ for weight-bearing cartilage volume, and r = 0.770–0.962 and mean differences = 0.513–1.138 mm for subregional cartilage thickness. With regard to osteoarthritis progression, our method found most of the significant associations identified using the manual segmentation method, for both 12- and 24-month subregional cartilage changes. The method may be effectively applied in osteoarthritis progression studies to extract cartilage-related imaging biomarkers

Patient radiation dose and fluoroscopy time during ERCP:a single-center, retrospective study of influencing factors

Abstract Objectives: Recently, both the number and the complexity with associated increased technical difficulty of therapeutic ERCP procedures have significantly increased resulting in longer procedural and fluoroscopy times. During ERCP, the patient is exposed to ionizing radiation and the consequent radiation dose depends on multiple factors. The aim of this study was to identify factors affecting fluoroscopy time and radiation dose in patients undergoing ERCP. Materials and methods: Data related to patient demographics, procedural characteristics and radiation exposure in ERCP procedures (n = 638) performed between August 2013 and August 2015 was retrospectively reviewed and analyzed. Statistically significant factors identified by univariate analyses were included in multivariate analysis with fluoroscopy time (FT) and dose area product (DAP) as dependent variables. Effective dose (ED) was estimated from DAP measurements using conversion coefficient. Results: The factors independently associated with increased DAP during ERCP were age, gender, radiographer, complexity level of ERCP, cannulation difficulty grade, bile duct injury and biliary stent placement. In multivariate analysis the endoscopist, the complexity level of ERCP, cannulation difficulty grade, pancreatic duct leakage, bile duct dilatation and brushing were identified as predictors for a longer FT. The mean DAP, FT, number of acquired images and ED for all ERCP procedures were 2.33 Gy·cm², 1.84 min, 3 and 0.61 mSv, respectively. Conclusions: Multiple factors had an effect on DAP and FT in ERCP. The awareness of these factors may help to predict possible prolonged procedures causing a higher radiation dose to the patient and thus facilitate the use of appropriate precautions