Automated Radiographic Measurements of Knee Osteoarthritis.

OBJECTIVE: Herewith, we report the development of Orthopedic Digital Image Analysis (ODIA) software that is developed to obtain quantitative measurements of knee osteoarthritis (OA) radiographs automatically. Manual segmentation and measurement of OA parameters currently hamper large-cohort analyses, and therefore, automated and reproducible methods are a valuable addition in OA research. This study aims to test the automated ODIA measurements and compare them with available manual Knee Imaging Digital Analysis (KIDA) measurements as comparison. DESIGN: This study included data from the CHECK (Cohort Hip and Cohort Knee) initiative, a prospective multicentre cohort study in the Netherlands with 1,002 participants. Knee radiographs obtained at baseline of the CHECK cohort were included and mean medial/lateral joint space width (JSW), minimal JSW, joint line convergence angle (JLCA), eminence heights, and subchondral bone intensities were compared between ODIA and KIDA. RESULTS: Of the potential 2,004 radiographs, 1,743 were included for analyses. Poor intraclass correlation coefficients (ICCs) were reported for the JLCA (0.422) and minimal JSW (0.299). The mean medial and lateral JSW, eminence height, and subchondral bone intensities reported a moderate to good ICC (0.7 or higher). Discrepancies in JLCA and minimal JSW between the 2 methods were mostly a problem in the lateral tibia plateau. CONCLUSIONS: The current ODIA tool provides important measurements of OA parameters in an automated manner from standard radiographs of the knee. Given the automated and computerized methodology that has very high reproducibility, ODIA is suitable for large epidemiological cohorts with various follow-up time points to investigate structural progression, such as CHECK or the Osteoarthritis Initiative (OAI).

Bone texture analysis for prediction of incident radiographic hip osteoarthritis using machine learning: data from the Cohort Hip and Cohort Knee (CHECK) study.

OBJECTIVE: To assess the ability of radiography-based bone texture variables in proximal femur and acetabulum to predict incident radiographic hip osteoarthritis (rHOA) over a 10 years period. DESIGN: Pelvic radiographs from CHECK at baseline (987 hips) were analyzed for bone texture using fractal signature analysis (FSA) in proximal femur and acetabulum. Elastic net (machine learning) was used to predict the incidence of rHOA (including Kellgren-Lawrence grade (KL) ≥ 2 or total hip replacement (THR)), joint space narrowing score (JSN, range 0-3), and osteophyte score (OST, range 0-3) after 10 years. Performance of prediction models was assessed using the area under the receiver operating characteristic curve (ROC AUC). RESULTS: Of the 987 hips without rHOA at baseline, 435 (44%) had rHOA at 10-year follow-up. Of the 667 hips with JSN grade 0 at baseline, 471 (71%) had JSN grade ≥ 1 at 10-year follow-up. Of the 613 hips with OST grade 0 at baseline, 526 (86%) had OST grade ≥ 1 at 10-year follow-up. AUCs for the models including age, gender, and body mass index (BMI) to predict incident rHOA, JSN, and OST were 0.59, 0.54, and 0.51, respectively. The inclusion of bone texture variables in the models improved the prediction of incident rHOA (ROC AUC 0.68 and 0.71 when baseline KL was also included in the model) and JSN (ROC AUC 0.62), but not incident OST (ROC AUC 0.52). CONCLUSION: Bone texture analysis provides additional information for predicting incident rHOA or THR over 10 years.

Aging does not change the compressive stiffness of mandibular condylar cartilage in horses.

OBJECTIVE: Aging can cause an increase in the stiffness of hyaline cartilage as a consequence of increased protein crosslinks. By induction of crosslinking, a reduction in the diffusion of solutions into the hyaline cartilage has been observed. However, there is a lack of knowledge about the effects of aging on the biophysical and biochemical properties of the temporomandibular joint (TMJ) cartilage. Hence, the aim of this study was to examine the biophysical properties (thickness, stiffness, and diffusion) of the TMJ condylar cartilage of horses of different ages and their correlation with biochemical parameters. MATERIALS AND METHODS: We measured the compressive stiffness of the condyles, after which the diffusion of two contrast agents into cartilage was measured using Contrast Enhanced Computed Tomography technique. Furthermore, the content of water, collagen, GAG, and pentosidine was analyzed. RESULTS: Contrary to our expectations, the stiffness of the cartilage did not change with age (modulus remained around 0.7 MPa). The diffusion of the negatively charged contrast agent (Hexabrix) also did not alter. However, the diffusion of the uncharged contrast agent (Visipaque) decreased with aging. The flux was negatively correlated with the amount of collagen and crosslink level which increased with aging. Pentosidine, collagen, and GAG were positively correlated with age whereas thickness and water content showed negative correlations. CONCLUSION: Our data demonstrated that aging was not necessarily reflected in the biophysical properties of TMJ condylar cartilage. The combination of the changes happening due to aging resulted in different diffusive properties, depending on the nature of the solution.

Micro- and nano-mechanics of osteoarthritic cartilage: The effects of tonicity and disease severity.

The present study aims to discover the contribution of glycosaminoglycans (GAGs) and collagen fibers to the mechanical properties of the osteoarthritic (OA) cartilage tissue. We used nanoindentation experiments to understand the mechanical behavior of mild and severe osteoarthritic cartilage at micro- and nano-scale at different swelling conditions. Contrast enhanced micro-computed tomography (EPIC-µCT) was used to confirm that mild OA specimens had significantly higher GAGs content compared to severe OA specimens. In micro-scale, the semi-equilibrium modulus of mild OA specimens significantly dropped after immersion in a hypertonic solution and at nano-scale, the histograms of the measured elastic modulus revealed three to four components. Comparing the peaks with those observed for healthy cartilage in a previous study indicated that the first and third peaks represent the mechanical properties of GAGs and the collagen network. The third peak shows considerably stiffer elastic modulus for mild OA samples as compared to the severe OA samples in isotonic conditions. Furthermore, this peak clearly dropped when the tonicity increased, indicating the loss of collagen (pre-) stress in the shrunk specimen. Our observations support the association of the third peak with the collagen network. However, our results did not provide any direct evidence to support the association of the first peak with GAGs. For severe OA specimens, the peak associated with the collagen network did not drop when the tonicity increased, indicating a change in the response of OA cartilage to hypertonicity, likely collagen damage, as the disease progresses to its latest stages.

Transport of neutral solute across articular cartilage: the role of zonal diffusivities.

Transport of solutes through diffusion is an important metabolic mechanism for the avascular cartilage tissue. Three types of interconnected physical phenomena, namely mechanical, electrical, and chemical, are all involved in the physics of transport in cartilage. In this study, we use a carefully designed experimental-computational setup to separate the effects of mechanical and chemical factors from those of electrical charges. Axial diffusion of a neutral solute Iodixanol into cartilage was monitored using calibrated microcomputed tomography micro-CT images for up to 48 hr. A biphasic-solute computational model was fitted to the experimental data to determine the diffusion coefficients of cartilage. Cartilage was modeled either using one single diffusion coefficient (single-zone model) or using three diffusion coefficients corresponding to superficial, middle, and deep cartilage zones (multizone model). It was observed that the single-zone model cannot capture the entire concentration-time curve and under-predicts the near-equilibrium concentration values, whereas the multizone model could very well match the experimental data. The diffusion coefficient of the superficial zone was found to be at least one order of magnitude larger than that of the middle zone. Since neutral solutes were used, glycosaminoglycan (GAG) content cannot be the primary reason behind such large differences between the diffusion coefficients of the different cartilage zones. It is therefore concluded that other features of the different cartilage zones such as water content and the organization (orientation) of collagen fibers may be enough to cause large differences in diffusion coefficients through the cartilage thickness.