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Abstract Long bone growth requires the precise control of chondrocyte maturation from proliferation to hypertrophy during endochondral ossification, but the bioenergetic program that ensures normal cartilage development is still largely elusive. We show that chondrocytes have unique glucose metabolism signatures in these stages, and they undergo bioenergetic reprogramming from glycolysis to oxidative phosphorylation during maturation, accompanied by an upregulation of the pentose phosphate pathway. Inhibition of either oxidative phosphorylation or the pentose phosphate pathway in murine chondrocytes and bone organ cultures impaired hypertrophic differentiation, suggesting that the appropriate balance of these pathways is required for cartilage development. Insulin-like growth factor 2 (IGF2) deficiency resulted in a profound increase in oxidative phosphorylation in hypertrophic chondrocytes, suggesting that IGF2 is required to prevent overactive glucose metabolism and maintain a proper balance of metabolic pathways. Our results thus provide critical evidence of preference for a bioenergetic pathway in different stages of chondrocytes and highlight its importance as a fundamental mechanism in skeletal development. 

In the medical sector, three-dimensional (3D) images are commonly used like computed tomography (CT) and magnetic resonance imaging (MRI). The 3D MRI is a non-invasive method of studying the soft-tissue structures in a knee joint for osteoarthritis studies. It can greatly improve the accuracy of segmenting structures such as cartilage, bone marrow lesion, and meniscus by identifying the bone structure first. U-net is a convolutional neural network that was originally designed to segment the biological images with limited training data. The input of the original U-net is a single 2D image and the output is a binary 2D image. In this study, we modified the U-net model to identify the knee bone structures using 3D MRI, which is a sequence of 2D slices. A fully automatic model has been proposed to detect and segment knee bones. The proposed model was trained, tested, and validated using 99 knee MRI cases where each case consists of 160 2D slices for a single knee scan. To evaluate the model’s performance, the similarity, dice coefficient (DICE), and area error metrics were calculated. Separate models were trained using different knee bone components including tibia, femur, patella, as well as a combined model for segmenting all the knee bones. Using the whole MRI sequence (160 slices), the method was able to detect the beginning and ending bone slices first, and then segment the bone structures for all the slices in between. On the testing set, the detection model accomplished 98.79% accuracy and the segmentation model achieved DICE 96.94% and similarity 93.98%. The proposed method outperforms several state-of-the-art methods, i.e., it outperforms U-net by 3.68%, SegNet by 14.45%, and FCN-8 by 2.34%, in terms of DICE score using the same dataset. 

Osteoarthritis (OA) is the most common form of arthritis and can often occur in the knee. While convolutional neural networks (CNNs) have been widely used to study medical images, the application of a 3-dimensional (3D) CNN in knee OA diagnosis is limited. This study utilizes a 3D CNN model to analyze sequences of knee magnetic resonance (MR) images to perform knee OA classification. An advantage of using 3D CNNs is the ability to analyze the whole sequence of 3D MR images as a single unit as opposed to a traditional 2D CNN, which examines one image at a time. Therefore, 3D features could be extracted from adjacent slices, which may not be detectable from a single 2D image. The input data for each knee were a sequence of double-echo steady-state (DESS) MR images, and each knee was labeled by the Kellgren and Lawrence (KL) grade of severity at levels 0–4. In addition to the 5-category KL grade classification, we further examined a 2-category classification that distinguishes non-OA (KL ≤ 1) from OA (KL ≥ 2) knees. Clinically, diagnosing a patient with knee OA is the ultimate goal of assigning a KL grade. On a dataset with 1100 knees, the 3D CNN model that classifies knees with and without OA achieved an accuracy of 86.5% on the validation set and 83.0% on the testing set. We further conducted a comparative study between MRI and X-ray. Compared with a CNN model using X-ray images trained from the same group of patients, the proposed 3D model with MR images achieved higher accuracy in both the 5-category classification (54.0% vs. 50.0%) and the 2-category classification (83.0% vs. 77.0%). The result indicates that MRI, with the application of a 3D CNN model, has greater potential to improve diagnosis accuracy for knee OA clinically than the currently used X-ray methods. 

Abstract Background We aimed to determine if composite structural measures of knee osteoarthritis (KOA) progression on magnetic resonance (MR) imaging can predict the radiographic onset of accelerated knee osteoarthritis. Methods We used data from a nested case-control study among participants from the Osteoarthritis Initiative without radiographic KOA at baseline. Participants were separated into three groups based on radiographic disease progression over 4 years: 1) accelerated (Kellgren-Lawrence grades [KL] 0/1 to 3/4), 2) typical (increase in KL, excluding accelerated osteoarthritis), or 3) no KOA (no change in KL). We assessed tibiofemoral cartilage damage (four regions: medial/lateral tibia/femur), bone marrow lesion (BML) volume (four regions: medial/lateral tibia/femur), and whole knee effusion-synovitis volume on 3 T MR images with semi-automated programs. We calculated two MR-based composite scores. Cumulative damage was the sum of standardized cartilage damage. Disease activity was the sum of standardized volumes of effusion-synovitis and BMLs. We focused on annual images from 2 years before to 2 years after radiographic onset (or a matched time for those without knee osteoarthritis). To determine between group differences in the composite metrics at all time points, we used generalized linear mixed models with group (3 levels) and time (up to 5 levels). For our prognostic analysis, we used multinomial logistic regression models to determine if one-year worsening in each composite metric change associated with future accelerated knee osteoarthritis (odds ratios [OR] based on units of 1 standard deviation of change). Results Prior to disease onset, the accelerated KOA group had greater average disease activity compared to the typical and no KOA groups and this persisted up to 2 years after disease onset. During a pre-radiographic disease period, the odds of developing accelerated KOA were greater in people with worsening disease activity [versus typical KOA OR (95% confidence interval [CI]): 1.58 (1.08 to 2.33); versus no KOA: 2.39 (1.55 to 3.71)] or cumulative damage [versus typical KOA: 1.69 (1.14 to 2.51); versus no KOA: 2.11 (1.41 to 3.16)]. Conclusions MR-based disease activity and cumulative damage metrics may be prognostic markers to help identify people at risk for accelerated onset and progression of knee osteoarthritis. 

Context : Ultrasound imaging is a clinically feasible tool to assess femoral articular cartilage and may have utility in tracking early knee osteoarthritis development. Traditional assessment techniques focus on measurements at a single location, which can be challenging to adopt for novice raters. Objective : To introduce a novel semiautomated ultrasound segmentation technique and determine the intrarater and interrater reliability of average regional femoral articular cartilage thickness and echo intensity of a novice and expert rater. Design : Descriptive observational study. Setting : Orthopedic clinic. Patients or Other Participants : Fifteen participants (mean [SD]; age 23.5 [4.6] y, height = 172.6 [9.3] cm, mass = 79.8 [15.7] kg) with a unilateral history of anterior cruciate ligament reconstruction participated. Intervention : None. Main Outcome Measures : One rater captured anterior femoral cartilage images of the participants’ contralateral knees using a transverse suprapatellar ultrasound assessment. The total femoral cartilage cross-sectional area of each image was segmented by a novice and expert rater. A novel custom program automatically separated the cartilage segmentations into medial, lateral, and intercondylar regions to determine the cross-sectional area and cartilage length. The average cartilage thickness in each region was calculated by dividing the cross-sectional area by the cartilage length. Echo intensity was calculated as the average gray-scale pixel value of each region. Two-way random effect intraclass correlations coefficient (ICC) for absolute agreement were used to determine the interrater reliability between a novice and expert rater, as well as the intrarater reliability of the novice rater. Results : The novice rater demonstrated excellent intrarater (ICC [ 2,k ] range = .993–.997) and interrater (ICC [ 2,k ] range = .944–.991) reliability with the expert rater of all femoral articular cartilage average thickness and echo intensity regions. Conclusions : The novel semiautomated average cartilage thickness and echo-intensity assessment is efficient, systematic, and reliable between an expert and novice rater with minimal training.