Deep Learning for Predicting Progression of Patellofemoral Osteoarthritis Based on Lateral Knee Radiographs, Demographic Data, and Symptomatic Assessments.

OBJECTIVE: In this study, we propose a novel framework that utilizes deep learning and attention mechanisms to predict the radiographic progression of patellofemoral osteoarthritis (PFOA) over a period of 7 years. MATERIAL AND METHODS: This study included subjects (1,832 subjects, 3,276 knees) from the baseline of the Multicenter Osteoarthritis Study (MOST). Patellofemoral joint regions of interest were identified using an automated landmark detection tool (BoneFinder) on lateral knee X-rays. An end-to-end deep learning method was developed for predicting PFOA progression based on imaging data in a five-fold cross-validation setting. To evaluate the performance of the models, a set of baselines based on known risk factors were developed and analyzed using gradient boosting machine (GBM). Risk factors included age, sex, body mass index, and Western Ontario and McMaster Universities Arthritis Index score, and the radiographic osteoarthritis stage of the tibiofemoral joint (Kellgren and Lawrence [KL] score). Finally, to increase predictive power, we trained an ensemble model using both imaging and clinical data. RESULTS: Among the individual models, the performance of our deep convolutional neural network attention model achieved the best performance with an area under the receiver operating characteristic curve (AUC) of 0.856 and average precision (AP) of 0.431, slightly outperforming the deep learning approach without attention (AUC = 0.832, AP = 0.4) and the best performing reference GBM model (AUC = 0.767, AP = 0.334). The inclusion of imaging data and clinical variables in an ensemble model allowed statistically more powerful prediction of PFOA progression (AUC = 0.865, AP = 0.447), although the clinical significance of this minor performance gain remains unknown. The spatial attention module improved the predictive performance of the backbone model, and the visual interpretation of attention maps focused on the joint space and the regions where osteophytes typically occur. CONCLUSION: This study demonstrated the potential of machine learning models to predict the progression of PFOA using imaging and clinical variables. These models could be used to identify patients who are at high risk of progression and prioritize them for new treatments. However, even though the accuracy of the models were excellent in this study using the MOST dataset, they should be still validated using external patient cohorts in the future.

Age and anatomical region-related differences in vascularization of the porcine meniscus using microcomputed tomography imaging.

Meniscal lesions in vascularized regions are known to regenerate while lack of vascular supply leads to poor healing. Here, we developed and validated a novel methodology for three-dimensional structural analysis of meniscal vascular structures with high-resolution microcomputed tomography (µCT). We collected porcine medial menisci from 10 neonatal (not-developed meniscus, n-) and 10 adults (fully developed meniscus, a-). The menisci were cut into anatomical regions (anterior horn (n-AH and a-AH), central body (n-CB and a-CB), and posterior horn (n-PH and a-PH). Specimens were cut in half, fixed, and one specimen underwent critical point drying and µCT imaging, while other specimen underwent immunohistochemistry and vascularity biomarker CD31 staining for validation of µCT. Parameters describing vascular structures were calculated from µCT. The vascular network in neonatal spread throughout meniscus, while in adult was limited to a few vessels in outer region, mostly on femoral side. n-AH, n-CB, and n-PH had 20, 17, and 11 times greater vascular volume fraction than adult, respectively. Moreover, thickness of blood vessels, in three regions, was six times higher in adults than in neonatal. a-PH appeared to have higher vascular fraction, longer and thicker blood vessels than both a-AH and a-CB. Overall, neonatal regions had a higher number of blood vessels, more branching, and higher tortuosity compared to adult regions. For the first time, critical point drying-based µCT imaging allowed detailed three-dimensional visualization and quantitative analysis of vascularized meniscal structures. We showed more vascularity in neonatal menisci, while adult menisci had fewer and thicker vascularity especially limited to the femoral surface.

Deep learning based detection of osteophytes in radiographs and magnetic resonance imagings of the knee using 2D and 3D morphology.

In this study, we investigated the discriminative capacity of knee morphology in automatic detection of osteophytes defined by the Osteoarthritis Research Society International atlas, using X-ray and magnetic resonance imaging (MRI) data. For the X-ray analysis, we developed a deep learning (DL) based model to segment femur and tibia. In case of MRIs, we utilized previously validated segmentations of femur, tibia, corresponding cartilage tissues, and menisci. Osteophyte detection was performed using DL models in four compartments: medial femur (FM), lateral femur (FL), medial tibia (TM), and lateral tibia (TL). To analyze the confounding effects of soft tissues, we investigated their morphology in combination with bones, including bones+cartilage, bones+menisci, and all the tissues. From X-ray-based 2D morphology, the models yielded balanced accuracy of 0.73, 0.69, 0.74, and 0.74 for FM, FL, TM, TL, respectively. Using 3D bone morphology from MRI, balanced accuracy was 0.80, 0.77, 0.71, and 0.76, respectively. The performance was higher than in 2D for all the compartments except for TM, with significant improvements observed for femoral compartments. Adding menisci or cartilage morphology consistently improved balanced accuracy in TM, with the greatest improvement seen for small osteophyte. Otherwise, the models performed similarly to bones-only. Our experiments demonstrated that MRI-based models show higher detection capability than X-ray based models for identifying knee osteophytes. This study highlighted the feasibility of automated osteophyte detection from X-ray and MRI data and suggested further need for development of osteophyte assessment criteria in addition to OARSI, particularly, for early osteophytic changes.

Clinical Super-Resolution Computed Tomography of Bone Microstructure: Application in Musculoskeletal and Dental Imaging.

PURPOSE: Clinical cone-beam computed tomography (CBCT) devices are limited to imaging features of half a millimeter in size and cannot quantify the tissue microstructure. We demonstrate a robust deep-learning method for enhancing clinical CT images, only requiring a limited set of easy-to-acquire training data. METHODS: Knee tissue from five cadavers and six total knee replacement patients, and 14 teeth from eight patients were scanned using laboratory CT as training data for the developed super-resolution (SR) technique. The method was benchmarked against ex vivo test set, 52 osteochondral samples are imaged with clinical and laboratory CT. A quality assurance phantom was imaged with clinical CT to quantify the technical image quality. To visually assess the clinical image quality, musculoskeletal and maxillofacial CBCT studies were enhanced with SR and contrasted to interpolated images. A dental radiologist and surgeon reviewed the maxillofacial images. RESULTS: The SR models predicted the bone morphological parameters on the ex vivo test set more accurately than conventional image processing. The phantom analysis confirmed higher spatial resolution on the SR images than interpolation, but image grayscales were modified. Musculoskeletal and maxillofacial CBCT images showed more details on SR than interpolation; however, artifacts were observed near the crown of the teeth. The readers assessed mediocre overall scores for both SR and interpolation. The source code and pretrained networks are publicly available. CONCLUSION: Model training with laboratory modalities could push the resolution limit beyond state-of-the-art clinical musculoskeletal and dental CBCT. A larger maxillofacial training dataset is recommended for dental applications.

Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting From Multimodal Data.

Deep neural networks are often applied to medical images to automate the problem of medical diagnosis. However, a more clinically relevant question that practitioners usually face is how to predict the future trajectory of a disease. Current methods for prognosis or disease trajectory forecasting often require domain knowledge and are complicated to apply. In this paper, we formulate the prognosis prediction problem as a one-to-many prediction problem. Inspired by a clinical decision-making process with two agents-a radiologist and a general practitioner - we predict prognosis with two transformer-based components that share information with each other. The first transformer in this framework aims to analyze the imaging data, and the second one leverages its internal states as inputs, also fusing them with auxiliary clinical data. The temporal nature of the problem is modeled within the transformer states, allowing us to treat the forecasting problem as a multi-task classification, for which we propose a novel loss. We show the effectiveness of our approach in predicting the development of structural knee osteoarthritis changes and forecasting Alzheimer's disease clinical status directly from raw multi-modal data. The proposed method outperforms multiple state-of-the-art baselines with respect to performance and calibration, both of which are needed for real-world applications. An open-source implementation of our method is made publicly available at https://github.com/Oulu-IMEDS/CLIMATv2.

Fourier-transform infrared study on effects of ageing, oestrogen level and altered dietary loading on rat mandibular condylar cartilage.

OBJECTIVE: Mandibular condylar cartilage (MCC) of the rat was examined with the Fourier-transform infrared (FITR) spectroscopic imaging to study the effects of ageing, oestrogen level and altered dietary loading on the structure of MCC. MATERIALS AND METHODS: The Sprague-Dawley rats (n = 96) aged 5 and 14 months were divided into 12 subgroups according to age, oestrogen status (ovariectomized [OVX], non-ovariectomized [non-OVX)]) and diet (hard, normal, soft). Specimens of the MCC were examined with FTIR spectroscopic imaging to quantify the distribution of collagens and proteoglycans. MCC was divided sagittally into three segments: anterior, most superior and posterior. From each segment, the collagen and proteoglycan contents at different depths of cartilage were statistically compared between the groups using an N-way analysis of variance (ANOVA). RESULTS: The amount of collagen content was significantly associated with old age in the deep layer of the anterior segment and in the middle layer of the posterior segment of MCC. In the deep layer of the most superior segment, the collagen content also increased with ageing. The amount of proteoglycan content increased significantly when dietary loading increased, and the oestrogen level decreased in the deep layer of the most superior segment of MCC. CONCLUSION: Ageing, oestrogen level and altered dietary loading have a significant effect on the location and content of collagens and proteoglycans of rat MCC. Ageing significantly increased the amount of collagen content in the superior and posterior segments, being highest in the older soft-diet rats. Decreased oestrogen levels and increased dietary loading increased the amount of proteoglycan content.

Semiautomatic Assessment of Facet Tropism From Lumbar Spine MRI Using Deep Learning: A Northern Finland Birth Cohort Study.

STUDY DESIGN: This is a retrospective, cross-sectional, population-based study that automatically measured the facet joint (FJ) angles from T2-weighted axial magnetic resonance imagings (MRIs) of the lumbar spine using deep learning (DL). OBJECTIVE: This work aimed to introduce a semiautomatic framework that measures the FJ angles using DL and study facet tropism (FT) in a large Finnish population-based cohort. SUMMARY OF DATA: T2-weighted axial MRIs of the lumbar spine (L3/4 through L5/S1) for (n=1288) in the NFBC1966 Finnish population-based cohort were used for this study. MATERIALS AND METHODS: A DL model was developed and trained on 430 participants' MRI images. The authors computed FJ angles from the model's prediction for each level, that is, L3/4 through L5/S1, for the male and female subgroups. Inter-rater and intrarater reliability was analyzed for 60 participants using annotations made by two radiologists and a musculoskeletal researcher. With the developed method, we examined FT in the entire NFBC1966 cohort, adopting the literature definitions of FT thresholds at 7° and 10°. The rater agreement was evaluated both for the annotations and the FJ angles computed based on the annotations. FJ asymmetry ( - was used to evaluate the agreement and correlation between the raters. Bland-Altman analysis was used to assess the agreement and systemic bias in the FJ asymmetry. The authors used the Dice score as the metric to compare the annotations between the raters. The authors evaluated the model predictions on the independent test set and compared them against the ground truth annotations. RESULTS: This model scored Dice (92.7±0.1) and intersection over union (87.1±0.2) aggregated across all the regions of interest, that is, vertebral body (VB), FJs, and posterior arch (PA). The mean FJ angles measured for the male and female subgroups were in agreement with the literature findings. Intrarater reliability was high, with a Dice score of VB (97.3), FJ (82.5), and PA (90.3). The inter-rater reliability was better between the radiologists with a Dice score of VB (96.4), FJ (75.5), and PA (85.8) than between the radiologists and the musculoskeletal researcher. The prevalence of FT was higher in the male subgroup, with L4/5 found to be the most affected region. CONCLUSION: The authors developed a DL-based framework that enabled us to study FT in a large cohort. Using the proposed method, the authors present the prevalence of FT in a Finnish population-based cohort.

Magnetic-Responsive Carbon Nanotubes Composite Scaffolds for Chondrogenic Tissue Engineering.

The demand for engineered scaffolds capable of delivering multiple cues to cells continues to grow as the interplay between cell fate with microenvironmental and external cues is revealed. Emphasis has been given to develop stimuli-responsive scaffolds. These scaffolds are designed to sense an external stimulus triggering a specific response (e.g., change in the microenvironment, release therapeutics, etc.) and then initiate/modulate a desired biofunction. Here, magnetic-responsive carboxylated multi-walled carbon nanotubes (cMWCNTs) are integrated into 3D collagen/polylactic acid (PLA) scaffold via a reproducible filtration-based method. The integrity and biomechanical performance of the collagen/PLA scaffolds are preserved after cMWCNT integration. In vitro safety assessment of cMWCNT/collagen/PLA scaffolds shows neither cytotoxicity effects nor macrophage pro-inflammatory response, supporting further in vitro studies. The cMWCNT/collagen/PLA scaffolds enhance chondrocytes metabolic activity while maintaining high cell viability and extracellular matrix (i.e., type II collagen and aggrecan) production. Comprehensive in vitro study applying static and pulsed magnetic field on seeded scaffolds shows no specific cell response in dependence with the applied field. This result is independent of the presence or absence of cMWCNT into the collagen/PLA scaffolds. Taken together, these findings provide additional evidence of the benefits to exploit the CNTs outstanding properties in the design of stimuli-responsive scaffolds.

Characterisation of Cartilage Damage via Fusing Mid-Infrared, Near-Infrared, and Raman Spectroscopic Data.

Mid-infrared spectroscopy (MIR), near-infrared spectroscopy (NIR), and Raman spectroscopy are all well-established analytical techniques in biomedical applications. Since they provide complementary chemical information, we aimed to determine whether combining them amplifies their strengths and mitigates their weaknesses. This study investigates the feasibility of the fusion of MIR, NIR, and Raman spectroscopic data for characterising articular cartilage integrity. Osteochondral specimens from bovine patellae were subjected to mechanical and enzymatic damage, and then MIR, NIR, and Raman data were acquired from the damaged and control specimens. We assessed the capacity of individual spectroscopic methods to classify the samples into damage or control groups using Partial Least Squares Discriminant Analysis (PLS-DA). Multi-block PLS-DA was carried out to assess the potential of data fusion by combining the dataset by applying two-block (MIR and NIR, MIR and Raman, NIR and Raman) and three-block approaches (MIR, NIR, and Raman). The results of the one-block models show a higher classification accuracy for NIR (93%) and MIR (92%) than for Raman (76%) spectroscopy. In contrast, we observed the highest classification efficiency of 94% and 93% for the two-block (MIR and NIR) and three-block models, respectively. The detailed correlative analysis of the spectral features contributing to the discrimination in the three-block models adds considerably more insight into the molecular origin of cartilage damage.

Micro-computed Tomography-Based Collagen Orientation and Anisotropy Analysis of Rabbit Articular Cartilage.

The collagen network is the highly organized backbone of articular cartilage providing tissue tensile stiffness and restricting proteoglycan bleaching out of the tissue. Osteoarthritis (OA) diminishes proper collagen network adaptation. Our aim was to provide quantitative three-dimensional (3D) information of the cartilage collagen network adaptation in early osteoarthritis using high resolution micro-computed tomography (µCT)-imaging. Osteochondral samples from the femoral condyles were collected from healthy (N = 8, both legs) and experimental OA rabbit model with anterior cruciate ligament transection (N = 14, single leg). Samples were processed for cartilage µCT-imaging and histological evaluation with polarized light microscopy (PLM). Structure tensor analysis was used to analyse the collagen fibre orientation and anisotropy of the µCT-images, and PLM was used as a validation for structural changes. Depth-wise comparison of collagen fibre orientation acquired with µCT-imaging and PLM correlated well, but the values obtained with PLM were systematically greater than those measured with µCT-imaging. Structure tensor analysis allowed for 3D quantification of collagen network anisotropy. Finally, µCT-imaging revealed only minor differences between the control and experimental groups.

The ultrasound assessment of osteoarthritis: the current status.

Traditionally, osteoarthritis (OA) is diagnosed with the clinical examination supplemented by the conventional radiography (CR). In the research literature, the role of ultrasound (US) imaging in the diagnostics of OA has risen steadily during the last two decades. US imaging is cheap and globally widely available often already in primary healthcare. Here, we reviewed the most essential US literature focusing on OA diagnostics and progression prediction using the various search engines. Starting from the year 2000, our search provided 1 445 journal articles. After reviewing the abstracts, 89 articles were finally included. Most of the reviewed articles focused on the imaging of knee and hand OA, whereas only a minority dealt with the imaging of hip, ankle, midfoot, acromioclavicular, and temporomandibular joints. Overall, during the last 20 years, the use of US imaging for OA assessment has increased in the scientific literature. In knee and hand joints, US imaging has been reported to be a promising tool to evaluate OA changes. Furthermore, the reproducibility of US as well as its association to MRI findings are excellent. Importantly, US seems to even outperform CR in certain aspects, such as detection of osteophytes, joint inflammation, meniscus protrusion, and localized cartilage damage (especially at the medial femoral condyle and sulcus area). Based on the reviewed literature, US can be truly considered as a complementary tool to CR in the clinical setup for OA diagnostics. New technical developments may even enhance the diagnostic value of the US in the future.

External Validation of SpineNet, an Open-Source Deep Learning Model for Grading Lumbar Disk Degeneration MRI Features, Using the Northern Finland Birth Cohort 1966.

STUDY DESIGN: This is a retrospective observational study to externally validate a deep learning image classification model. OBJECTIVE: Deep learning models such as SpineNet offer the possibility of automating the process of disk degeneration (DD) classification from magnetic resonance imaging (MRI). External validation is an essential step to their development. The aim of this study was to externally validate SpineNet predictions for DD using Pfirrmann classification and Modic changes (MCs) on data from the Northern Finland Birth Cohort 1966 (NFBC1966). SUMMARY OF DATA: We validated SpineNet using data from 1331 NFBC1966 participants for whom both lumbar spine MRI data and consensus DD gradings were available. MATERIALS AND METHODS: SpineNet returned Pfirrmann grade and MC presence from T2-weighted sagittal lumbar MRI sequences from NFBC1966, a data set geographically and temporally separated from its training data set. A range of agreement and reliability metrics were used to compare predictions with expert radiologists. Subsets of data that match SpineNet training data more closely were also tested. RESULTS: Balanced accuracy for DD was 78% (77%-79%) and for MC 86% (85%-86%). Interrater reliability for Pfirrmann grading was Lin concordance correlation coefficient=0.86 (0.85-0.87) and Cohen κ=0.68 (0.67-0.69). In a low back pain subset, these reliability metrics remained largely unchanged. In total, 20.83% of disks were rated differently by SpineNet compared with the human raters, but only 0.85% of disks had a grade difference >1. Interrater reliability for MC detection was κ=0.74 (0.72-0.75). In the low back pain subset, this metric was almost unchanged at κ=0.76 (0.73-0.79). CONCLUSIONS: In this study, SpineNet has been benchmarked against expert human raters in the research setting. It has matched human reliability and demonstrates robust performance despite the multiple challenges facing model generalizability.

Adaptation of Fibril-Reinforced Poroviscoelastic Properties in Rabbit Collateral Ligaments 8 Weeks After Anterior Cruciate Ligament Transection.

Ligaments of the knee provide stability and prevent excessive motions of the joint. Rupture of the anterior cruciate ligament (ACL), a common sports injury, results in an altered loading environment for other tissues in the joint, likely leading to their mechanical adaptation. In the collateral ligaments, the patterns and mechanisms of biomechanical adaptation following ACL transection (ACLT) remain unknown. We aimed to characterize the adaptation of elastic and viscoelastic properties of the lateral and medial collateral ligaments eight weeks after ACLT. Unilateral ACLT was performed in six rabbits, and collateral ligaments were harvested from transected and contralateral knee joints after eight weeks, and from an intact control group (eight knees from four animals). The cross-sectional areas were measured with micro-computed tomography. Stepwise tensile stress-relaxation testing was conducted up to 6% final strain, and the elastic and viscoelastic properties were characterized with a fibril-reinforced poroviscoelastic material model. We found that the cross-sectional area of the collateral ligaments in the ACL transected knees increased, the nonlinear elastic collagen network modulus of the LCL decreased, and the amount of fast relaxation in the MCL decreased. Our results indicate that rupture of the ACL leads to an early adaptation of the elastic and viscoelastic properties of the collagen fibrillar network in the collateral ligaments. These adaptations may be important to consider when evaluating whole knee joint mechanics after ACL rupture, and the results aid in understanding the consequences of ACL rupture on other tissues.

Predicting total knee arthroplasty from ultrasonography using machine learning.

Objective: To investigate the value of ultrasonographic data in predicting total knee replacement (TKR). Design: Data from the Musculoskeletal Pain in Ullensaker study (MUST) was linked to the Norwegian Arthroplasty Register to form a 5-7 year prospective cohort study of 630 persons (69% women, mean (SD) age 64 (8.7) years). We examined the predictive power of ultrasound (US) features, i.e. osteophytes, meniscal extrusion, synovitis in the suprapatellar recess, femoral cartilage thickness, and quality for future knee osteoarthritis (OA) surgery. We investigated 4 main settings for multivariate predictive modeling: 1) clinical predictors (age, sex, body mass index, knee injury, familial OA and workload), 2) radiographic data (assessed by the Kellgren Lawrence grade, KL) with clinical predictors, 3) US features and clinical predictors. Finally, we also considered an ensemble of models 2) and 3) and used it as our fifth model. All models were compared using the Average Precision (AP) and the Area Under Receiver Operating Characteristic Curve (AUC) metrics. Results: Clinical predictors yielded AP of 0.11 (95% confidence interval [CI] 0.05-0.23) and AUC of 0.69 (0.58-0.79). Clinical predictors with KL grade yielded AP of 0.20 (0.12-0.33) and AUC of 0.81 (0.67-0.90). The clinical variables with ultrasound yielded AP of 0.17 (0.08-0.30) and AUC of 0.79 (0.69-0.86). Conclusion: Ultrasonographic examination of the knee may provide added value to basic clinical and demographic descriptors when predicting TKR. While it does not achieve the same predictive performance as radiography, it can provide additional value to the radiographic examination.

Diagnostic performance of attenuated total reflection Fourier-transform infrared spectroscopy for detecting COVID-19 from routine nasopharyngeal swab samples.

Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy coupled with machine learning-based partial least squares discriminant analysis (PLS-DA) was applied to study if severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could be detected from nasopharyngeal swab samples originally collected for polymerase chain reaction (PCR) analysis. Our retrospective study included 558 positive and 558 negative samples collected from Northern Finland. Overall, we found moderate diagnostic performance for ATR-FTIR when PCR analysis was used as the gold standard: the average area under the receiver operating characteristics curve (AUROC) was 0.67-0.68 (min. 0.65, max. 0.69) with 20, 10 and 5 k-fold cross validations. Mean accuracy, sensitivity and specificity was 0.62-0.63 (min. 0.60, max. 0.65), 0.61 (min. 0.58, max. 0.65) and 0.64 (min. 0.59, max. 0.67) with 20, 10 and 5 k-fold cross validations. As a conclusion, our study with relatively large sample set clearly indicate that measured ATR-FTIR spectrum contains specific information for SARS-CoV-2 infection (P < 0.001 for AUROC in label permutation test). However, the diagnostic performance of ATR-FTIR remained only moderate, potentially due to low concentration of viral particles in the transport medium. Further studies are needed before ATR-FTIR can be recommended for fast screening of SARS-CoV-2 from nasopharyngeal swab samples.

Associations of human femoral condyle cartilage structure and composition with viscoelastic and constituent-specific material properties at different stages of osteoarthritis.

The relationships between structure and function in human knee femoral cartilage are not well-known at different stages of osteoarthritis. Thus, our aim was to characterize the depth-dependent composition and structure (proteoglycan content, collagen network organization and collagen content) of normal and osteoarthritic human femoral condyle cartilage (n = 47) and relate them to their viscoelastic and constituent-specific mechanical properties that are obtained through dynamic sinusoidal testing and fibril-reinforced poroelastic material modeling of stress-relaxation testing, respectively. We characterized the proteoglycan content using digital densitometry, collagen network organization (orientation angle and anisotropy) using polarized light microscopy and collagen content using Fourier transform infrared spectroscopy. In the superficial cartilage (0-10 % of thickness), the collagen network disorganization and proteoglycan loss were associated with the smaller initial fibril network modulus - a parameter representing the pretension of the collagen network. Furthermore, the proteoglycan loss was associated with the greater strain-dependent fibril network modulus - a measure of nonlinear mechanical behavior. The proteoglycan loss was also associated with greater cartilage viscosity at a low loading frequency (0.005 Hz), while the collagen network disorganization was associated with greater cartilage viscosity at a high loading frequency (1 Hz). Our results suggest that proteoglycan loss and collagen network disorganization reduce the pretension of the collagen network while proteoglycan degradation also increases the nonlinear mechanical behavior of the collagen network. Further, the results also highlight that proteoglycan loss and collagen disorganization increase the viscosity of femoral cartilage, but their contribution to increased viscosity occurs in completely different loading frequencies.

Assessment of Ligament Viscoelastic Properties Using Raman Spectroscopy.

Injuries to the ligaments of the knee commonly impact vulnerable and physically active individuals. These injuries can lead to the development of degenerative diseases such as post-traumatic osteoarthritis (PTOA). Non-invasive optical modalities, such as infrared and Raman spectroscopy, provide means for quantitative evaluation of knee joint tissues and have been proposed as potential quantitative diagnostic tools for arthroscopy. In this study, we evaluate Raman spectroscopy as a viable tool for estimating functional properties of collateral ligaments. Artificial trauma was induced by anterior cruciate ligament transection (ACLT) in the left or right knee joint of skeletally mature New Zealand rabbits. The corresponding contralateral (CL) samples were extracted from healthy unoperated joints along with a separate group of control (CNTRL) animals. The rabbits were sacrificed at 8 weeks after ACLT. The ligaments were then harvested and measured using Raman spectroscopy. A uniaxial tensile stress-relaxation testing protocol was adopted for determining several biomechanical properties of the samples. Partial least squares (PLS) regression models were then employed to correlate the spectral data with the biomechanical properties. Results show that the capacity of Raman spectroscopy for estimating the biomechanical properties of the ligament samples varies depending on the target property, with prediction error ranging from 15.78% for tissue cross-sectional area to 30.39% for stiffness. The hysteresis under cyclic loading at 2 Hz (RMSE = 6.22%, Normalized RMSE = 22.24%) can be accurately estimated from the Raman data which describes the viscous damping properties of the tissue. We conclude that Raman spectroscopy has the potential for non-destructively estimating ligament biomechanical properties in health and disease, thus enhancing the diagnostic value of optical arthroscopic evaluations of ligament integrity.

Mineral Crystal Thickness in Calcified Cartilage and Subchondral Bone in Healthy and Osteoarthritic Human Knees.

Osteoarthritis (OA) is the most common joint disease, where articular cartilage degradation is often accompanied with sclerosis of the subchondral bone. However, the association between OA and tissue mineralization at the nanostructural level is currently not understood. In particular, it is technically challenging to study calcified cartilage, where relevant but poorly understood pathological processes such as tidemark multiplication and advancement occur. Here, we used state-of-the-art microfocus small-angle X-ray scattering with a 5-µm spatial resolution to determine the size and organization of the mineral crystals at the nanostructural level in human subchondral bone and calcified cartilage. Specimens with a wide spectrum of OA severities were acquired from both medial and lateral compartments of medial compartment knee OA patients (n = 15) and cadaver knees (n = 10). Opposing the common notion, we found that calcified cartilage has thicker and more mutually aligned mineral crystals than adjoining bone. In addition, we, for the first time, identified a well-defined layer of calcified cartilage associated with pathological tidemark multiplication, containing 0.32 nm thicker crystals compared to the rest of calcified cartilage. Finally, we found 0.2 nm thicker mineral crystals in both tissues of the lateral compartment in OA compared with healthy knees, indicating a loading-related disease process because the lateral compartment is typically less loaded in medial compartment knee OA. In summary, we report novel changes in mineral crystal thickness during OA. Our data suggest that unloading in the knee might be involved with the growth of mineral crystals, which is especially evident in the calcified cartilage. © 2022 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Preclassification of Broadband and Sparse Infrared Data by Multiplicative Signal Correction Approach.

Preclassification of raw infrared spectra has often been neglected in scientific literature. Separating spectra of low spectral quality, due to low signal-to-noise ratio, presence of artifacts, and low analyte presence, is crucial for accurate model development. Furthermore, it is very important for sparse data, where it becomes challenging to visually inspect spectra of different natures. Hence, a preclassification approach to separate infrared spectra for sparse data is needed. In this study, we propose a preclassification approach based on Multiplicative Signal Correction (MSC). The MSC approach was applied on human and the bovine knee cartilage broadband Fourier Transform Infrared (FTIR) spectra and on a sparse data subset comprising of only seven wavelengths. The goal of the preclassification was to separate spectra with analyte-rich signals (i.e., cartilage) from spectra with analyte-poor (and high-matrix) signals (i.e., water). The human datasets 1 and 2 contained 814 and 815 spectra, while the bovine dataset contained 396 spectra. A pure water spectrum was used as a reference spectrum in the MSC approach. A threshold for the root mean square error (RMSE) was used to separate cartilage from water spectra for broadband and the sparse spectral data. Additionally, standard noise-to-ratio and principle component analysis were applied on broadband spectra. The fully automated MSC preclassification approach, using water as reference spectrum, performed as well as the manual visual inspection. Moreover, it enabled not only separation of cartilage from water spectra in broadband spectral datasets, but also in sparse datasets where manual visual inspection cannot be applied.

Rapid X-Ray-Based 3-D Finite Element Modeling of Medial Knee Joint Cartilage Biomechanics During Walking.

Finite element (FE) modeling is becoming an increasingly popular method for analyzing knee joint mechanics and biomechanical mechanisms leading to osteoarthritis (OA). The most common and widely available imaging method for knee OA diagnostics is planar X-ray imaging, while more sophisticated imaging methods, e.g., magnetic resonance imaging (MRI) and computed tomography (CT), are seldom used. Hence, the capability to produce accurate biomechanical knee joint models directly from X-ray imaging would bring FE modeling closer to clinical use. Here, we extend our atlas-based framework by generating FE knee models from X-ray images (N = 28). Based on measured anatomical landmarks from X-ray and MRI, knee joint templates were selected from the atlas library. The cartilage stresses and strains of the X-ray-based model were then compared with the MRI-based model during the stance phase of the gait. The biomechanical responses were statistically not different between MRI- vs. X-ray-based models when the template obtained from X-ray imaging was the same as the MRI template. However, if this was not the case, the peak values of biomechanical responses were statistically different between X-ray and MRI models. The developed X-ray-based framework may pave the way for a clinically feasible approach for knee joint FE modeling.