Osteoarthritis imaging最新文献

{"title":"Beyond the algorithms: Challenges in predicting knee osteoarthritis pain from a machine learning point of view","authors":"Neslihan Bayramoglu ,&nbsp;Martin Englund ,&nbsp;Ida K. Haugen ,&nbsp;Muneaki Ishijima ,&nbsp;Simo Saarakkala","doi":"10.1016/j.ostima.2025.100381","DOIUrl":"10.1016/j.ostima.2025.100381","url":null,"abstract":"<div><h3>Background:</h3><div>Knee osteoarthritis (OA) presents complex pain experiences often poorly correlated with structural imaging findings. Machine learning (ML) and deep learning (DL) hold promise for predicting and identifying both current and future pain and personalizing interventions, but translating these insights into clinical practice remains challenging.</div></div><div><h3>Methods:</h3><div>This study highlights key challenges in applying ML and DL to predict knee OA pain, with a focus on the complex relationship between imaging features and subjective pain experiences. While not based on a systematic search or formal data extraction, the review aims to provide an informed overview of current issues and research directions.</div></div><div><h3>Results:</h3><div>Key challenges include the limited availability of longitudinal data, small and imbalanced datasets, and the difficulty of aligning multimodal data over time. Existing pain assessment tools often fail to capture the dynamic and multidimensional nature of pain. In addition, many studies underrepresent diverse populations and neglect psychosocial factors, which weakens model robustness and limits generalizability.</div></div><div><h3>Conclusions:</h3><div>Future research should integrate longitudinal and real-time data (e.g., wearables) and promote diverse, collaborative datasets to bridge the gap between ML-based predictions and clinical applicability in knee OA pain management.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 2","pages":"Article 100381"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148208390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adverse patellofemoral morphology and worsening osteoarthritis: an exploration of composite vs. single MRI outcomes","authors":"Brooke McGinley ,&nbsp;Michael P․ LaValley ,&nbsp;Johannes M․ Sieberer ,&nbsp;John Fulkerson ,&nbsp;John Lynch ,&nbsp;Ali Guermazi ,&nbsp;Frank W․ Roemer ,&nbsp;C․Elizabeth Lewis ,&nbsp;Neil Segal ,&nbsp;David T․ Felson ,&nbsp;for MOST Study Investigators","doi":"10.1016/j.ostima.2025.100383","DOIUrl":"10.1016/j.ostima.2025.100383","url":null,"abstract":"<div><h3>Objective</h3><div>To evaluate whether a composite outcome measure could better detect the effects of adverse patellofemoral morphology on knee structure over two-years than single outcome measures.</div></div><div><h3>Methods</h3><div>We used data from the Multicenter Osteoarthritis Study (MOST) to analyze the association between measures of patellofemoral morphology and composite outcomes that combined data on cartilage damage and bone marrow lesion enlargement in two subregions of the patellofemoral joint using proportional odds regression to account for ordinality. We used Z scores to assess sensitivity to change.</div></div><div><h3>Results</h3><div>In the cohort of 240 MOST participants with a mean age of 51 years, BMI of 29 kg/m² we found that for tibial tubercle to trochlear groove distance (TT-TG), entry point to trochlear groove angle (EPTG), and entry point to transition point angle (EPTP) cartilage worsening as a single measurement outperformed composite outcomes with higher Z scores. Alternatively, we found that patella tilt angle (PTA) performed best when predicting bone marrow lesion worsening as a single measurement than compared to the composite outcomes.</div></div><div><h3>Conclusion</h3><div>For patellofemoral morphological measures, composite outcomes that combined data on cartilage damage and bone marrow lesion enlargement failed to increase sensitivity to change over outcomes for single structures alone. Other composite outcomes may be more successful in increasing the sensivitiy to change, and this warrants further investigation.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 2","pages":"Article 100383"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148208391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel method of determining bone mineral density of the knee from a pre-surgical CT scan to aid in surgical planning","authors":"Niall Maguire ,&nbsp;Jared Weir ,&nbsp;Sebastien Lustig ,&nbsp;Alan Brett","doi":"10.1016/j.ostima.2026.100391","DOIUrl":"10.1016/j.ostima.2026.100391","url":null,"abstract":"<div><h3>Introduction</h3><div>Patients with reduced bone mineral density (BMD) may have poorer outcomes after cementless joint replacement surgery. BMD phantoms may be used to calibrate Hounsfield units (HU) to BMD values, but BMD assessment with phantoms is not routine prior to surgery. We describe automated BMD calibration using air, fat and an aluminum motion-detection rod in Mako robotic-assisted knee arthroplasty 3D CT image volumes.</div></div><div><h3>Methods</h3><div>Using 6111 CT knee arthroplasty planning image volumes, including 133 with a BMD phantom (Mindways, TX), mean BMD values were determined for air, fat, and bone equivalent rods as calibration standards. Image voxels containing air were identified using histogram analysis. The rod surface was identified using an Active Appearance Model (AAM) search. At each point on the AAM-segmented femoral bone surface, lines perpendicular to the surface (“normal rays) were sampled and gaussian mixture model histogram analysis was used to identify fat. Ten iterations of 10-fold cross-validation were performed to compare calibration using either the phantom or the air, fat, and rod standards in tibial and femoral trabecular bone.</div></div><div><h3>Results</h3><div>Trabecular bone BMD from phantom calibrations were highly correlated with BMD values calculated using the air, fat, and rod standards for femur and tibia (<em>r</em> ≈ 0.98). Bland-Altman analysis showed negligible bias of ∼0.3 mg/cm3. The standard deviation of differences was 9.9. mg/cm3, suggesting an upper bound of precision of 9 mg/cm3 (precision error for Mindways phantom is 3.6 mg/cm3), which produces a coefficient of variation of 7.7% at mean BMD=117 mg/cm3.</div></div><div><h3>Conclusion</h3><div>Accurate volumetric BMD values may be derived at the knee from CT image volumes routinely acquired before robotic-assisted arthroplasty. This information may be useful in informing surgical decisions regarding the use of cementless implants, particularly in patients at risk of osteoporosis.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 2","pages":"Article 100391"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148208392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Baseline 3D joint space width on weight-bearing CT in comparison with 2D JSWx on radiographs for predicting 24-month worsening of knee structure in the multicenter osteoarthritis study","authors":"Ryo Yoshikawa ,&nbsp;Ali Guermazi ,&nbsp;Irina Tolstykh ,&nbsp;Donald D. Anderson ,&nbsp;John A. Lynch ,&nbsp;Jeffrey Duryea ,&nbsp;Frank Roemer ,&nbsp;Michael C. Nevitt ,&nbsp;Neil A. Segal","doi":"10.1016/j.ostima.2025.100389","DOIUrl":"10.1016/j.ostima.2025.100389","url":null,"abstract":"<div><h3>Objective</h3><div>This study evaluated the predictive validity of baseline 3D joint space width (3D-JSW) measured using weight-bearing computed tomography (WBCT) versus 2D-JSW on radiographs for predicting knee structural worsening over 24 months, assessed by radiographs and MRI.</div></div><div><h3>Design</h3><div>A prospective observational study using data from the Multicenter Osteoarthritis Study (MOST) included 425 knees. Baseline 3D-JSW was assessed using WBCT acquired in a standing fixed-flexion position, and 2D-JSW was assessed from standing fixed-flexion posteroanterior radiographs. Outcomes included 24-month changes in tibiofemoral Osteoarthritis Research Society International (OARSI) joint space narrowing (JSN) grade and MRI Osteoarthritis Knee Score Cartilage Morphology (MOAKS-CM). Predictive validity was evaluated using logistic regression and comparison of areas under ROC curves.</div></div><div><h3>Results</h3><div>The study population (N=302) had a mean age of 63.2 years (standard deviation 8.9) and BMI of 28.2 kg/m² (standard deviation 4.8), with radiographic OA observed in 45.9 %. For medial JSN worsening, 2D-JSW (AUC=0.711) showed significantly better predictive validity than the basic demographic model (AUC=0.626, p=0.032), while 3D-JSW (AUC=0.681) did not outperform 2D-JSW (p=0.184). For lateral JSN worsening, 3D-JSW (AUC=0.900) and 2D-JSW (AUC=0.893) were similar (p=0.814). For medial MOAKS-CM worsening, 3D-JSW (AUC=0.729) and 2D-JSW (AUC=0.742) were similar (p=0.579), as were 3D-JSW (AUC=0.699) and 2D-JSW (AUC=0.692) for lateral worsening (p=0.779).</div></div><div><h3>Conclusions</h3><div>3D-JSW did not show superior predictive validity over 2D-JSW in predicting knee structural worsening over 24 months in a cohort predominatly without radiographic knee OA. WBCT remains a promising imaging modality for osteoarthritis assessment. Further studies are needed to determine its role as a diagnostic tool.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 2","pages":"Article 100389"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148208435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Associations between habitual walking speed and in vivo tibial cartilage strain in individuals with anterior cruciate ligament reconstruction","authors":"Caroline Lisee ,&nbsp;David Lalush ,&nbsp;Cortney Armitano-Lago ,&nbsp;Elizabeth Bjornsen ,&nbsp;Jason R. Franz ,&nbsp;Jeffrey T. Spang ,&nbsp;Matthew E. Dausch ,&nbsp;Brian Pietrosimone","doi":"10.1016/j.ostima.2025.100390","DOIUrl":"10.1016/j.ostima.2025.100390","url":null,"abstract":"<div><h3>Objectives</h3><div>The purpose of the study was to determine associations between habitual walking speed and an in vivo magnetic resonance imagining (MRI) measure of tibial cartilage strain following primary anterior cruciate ligament reconstruction (ACLR).</div></div><div><h3>Methods</h3><div>Habitual walking speed was collected over ground in 12 individuals (5 Males/7 Females). Participants underwent an MRI of their ACLR knee to capture resting tibial articular cartilage thickness. Following the MRI, participants walked on a treadmill at their habitual walking speed for 3000 steps. Cartilage thickness was calculated for pre-loading and post-loading across the entire articulating surface and strain maps were created by calculating the change in thickness from pre- to post-loading thickness. Average cartilage strain was calculated in 6 evenly divided regions of interest (ROI) of the medial and lateral tibial cartilage. Spearman’s rank correlations and 95 % confidence intervals (CIs) (ρ) were used to assess associations between habitual walking speed and average tibial articular cartilage strain in each ROI.</div></div><div><h3>Results</h3><div>Slower habitual walking speed was moderately associated with greater strain in medial central inner tibial cartilage region (ρ=-0.67, <em>95 %CI=[-90,-0.14]</em>). All other associations between cartilage strain and medial (ρ range= -0.30 to 0.06, <em>95 %CI</em> range=-0.75 to 0.62) or lateral (ρ range= -0.42 to 0.28, <em>95 %CI</em> range=-0.81 to 0.74) tibial cartilage ROIs were weak to moderate.</div></div><div><h3>Conclusion</h3><div>Our study demonstrates a measurable in vivo link between slower habitual walking speeds and greater cartilage strain, which provides further rationale for evaluating the mechanistic link between modifying habitual walking speed to influence important biophysical changes in cartilage health.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 1","pages":"Article 100390"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147552487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing intra-rater reliability of peripheral quantitative computed tomography in knee joint bone evaluation on individuals with and without obesity: A GRRAS study","authors":"Reece Blay ,&nbsp;Christopher Wichman ,&nbsp;Yvonne M. Golightly ,&nbsp;Laura Bilek","doi":"10.1016/j.ostima.2026.100393","DOIUrl":"10.1016/j.ostima.2026.100393","url":null,"abstract":"<div><h3>Objective</h3><div>Common imaging approaches for knee osteoarthritis (KOA) do not reliably assess early bone changes. Obesity is a major KOA risk factor and can complicate imaging due to positioning and image quality. This pilot study primarily aimed to assess the intra-rater reliability of peripheral quantitative computed tomography (pQCT) for evaluating bone at the distal femur and proximal tibia, with a secondary objective of comparing reliability metrics between individuals with and without obesity.</div></div><div><h3>Design</h3><div>52 participants (81% female, mean age 50 [range 23-79] years) were matched by body mass index (BMI) (obese: BMI ≥ 30 kg/m², non-obese: BMI &lt; 30 kg/m²), within ± 5 years of age, sex, and &gt;5 kg/m² BMI difference. A single technician acquired two independent pQCT scans (Stratec XCT 3000) of the right tibiofemoral joint during one visit. Reliability was assessed using coefficient of variation root mean squared (CV_rms), reliability coefficient (R), and Bland-Altman analyses for repeated bone density (total, trabecular, cortical) and cross-sectional area (CSA) measures overall and by obesity group.</div></div><div><h3>Results</h3><div>pQCT demonstrated no systematic bias overall, with no substantial differences in reliability between obesity groups. Density measures showed excellent precision at all tibial regions and the medial femoral condyle. Tibial CSA measures showed no systematic bias but low precision. CSA measures at the medial femoral condyle were unreliable, and all measures at the lateral femoral condyle were unreliable due to inconsistent image capture.</div></div><div><h3>Conclusion</h3><div>pQCT is a potential tool to reliably detect early KOA-related bone changes, with no substantial differences in reliability between individuals with and without obesity.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 1","pages":"Article 100393"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147552488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disruption of the microenvironmental ecosystem in subchondral bone marrow lesions: Roles in osteoarthritis pathophysiology, pain and progression","authors":"Ali Mobasheri ,&nbsp;João Henriques ,&nbsp;Jamie E. Collins","doi":"10.1016/j.ostima.2025.100374","DOIUrl":"10.1016/j.ostima.2025.100374","url":null,"abstract":"<div><div>This narrative review explores the role of subchondral bone in the pathophysiology of osteoarthritis (OA), emphasizing the biomechanical and biochemical contributions that it makes to disease progression. A fresh perspective is presented by the authors in the form of a novel hypothesis that implicates mechano-inflammatory disruption of the bone marrow (BM) microenvironmental ecosystem (BM niche), unresolved inflammation, and fibrosis as key drivers of OA pathogenesis and pain. BMLs, detectable via magnetic resonance imaging (MRI), serve as dynamic imaging biomarkers that correlate with pain severity and structural deterioration, making them valuable in patient stratification for enhanced clinical trials and more effective therapeutic monitoring. Persistent inflammation within the BM microenvironmental niche exacerbates nociceptive signaling and structural joint damage. The parallels between OA, hematopoietic malignancies and immunological disorders of the BM underscore the potential for targeting inflammatory and fibrotic pathways within the BM niche as novel therapeutic strategies. Additionally, obesity-associated BM dysfunction raises the prospect of weight-loss interventions, including GLP-1 receptor agonists, as potential disease-modifying approaches for OA. We propose that the future research agenda should focus on integrating imaging biomarkers with genomic, molecular and clinical data to explore alterations in the BM microenvironmental ecosystem. This new knowledge may refine precision medicine strategies, improve early detection, enhance prognostic monitoring and develop targeted therapies aimed at successful modification of OA progression and effective control of pain mechanisms.</div></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 1","pages":"Article 100374"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147552489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: Special issue imaging and pain","authors":"Mohamed Jarraya ,&nbsp;Mylène P Jansen","doi":"10.1016/j.ostima.2026.100392","DOIUrl":"10.1016/j.ostima.2026.100392","url":null,"abstract":"","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 1","pages":"Article 100392"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147552486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FULLY AUTOMATED DEEP LEARNING PIPELINE FOR HAND OSTEOARTHRITIS CLASSIFICATION REPLICATING THE OARSI ATLAS GRADING SYSTEM","authors":"M. Roba Fraguas,&nbsp;J.B. Driban,&nbsp;T.E. McAlindon,&nbsp;I.K. Haugen,&nbsp;S.M. Bahouth,&nbsp;M.B. Roberts,&nbsp;J. Pascau,&nbsp;M. García-Sevilla,&nbsp;J. Duryea","doi":"10.1016/j.ostima.2026.100412","DOIUrl":"10.1016/j.ostima.2026.100412","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;INTRODUCTION&lt;/h3&gt;&lt;div&gt;While the OARSI atlas grading system is useful for studies of hand OA, it is time consuming, expensive, and requires an expert reader.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;To validate a fully automated Deep Learning (DL) pipeline that detects hands, standardizes hand orientation, detects radiographic R/L markers, locates and crops relevant joints, and performs OARSI JSN and OST grading classification at both binary (grade 0 or &gt; 0) and multiclass (grades 0–3) levels.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;Using 6810 OAI PA dominant hand radiographs (3405 participants) with available OARSI scoring, we developed a fully automated pipeline composed of five sequential stages. First, YOLOv8n object detection models were trained to detect radiographic R/L laterality markers hand orientation. During hand orientation training, marker regions were masked to prevent shortcut learning. Second, detected markers were assigned to the nearest hand based on bounding box centroid distance to determine anatomical laterality. Third, for joint localization, the HRNet-w32 model was trained on a manually annotated dataset of 987 images, including hand bounding boxes and 19 clinically relevant keypoints corresponding to the CMC, MCP, PIP, DIP, and thumb IP joints across the five digits. Fourth, data from stages 1-3 were used to generate individual joint-level crops. Fifth, DIP, PIP, and MCP joint crops from digits 2–5 with available OARSI labels were selected for JSN and OST classification. OARSI grade distributions were: JSN, grade 0: N=65,444, grade 1: N=12,687, grade 2: N=2,315, grade 3: N=1,219; OST, grade 0: N=62,891, grade 1: N=16,993, grade 2: N=1,539, grade 3: N=542. These crops were then used to train a ConvNeXt-Tiny model for binary and multiclass OARSI-based classification. Hand and marker detection were assessed using mAP50 and macro F1-score, and keypoint localization performance was evaluated using successful detection rate (SDR), as summarized in Figure 1. Final OARSI JSN and OST classification performance was evaluated using the macro F1-score, with detailed metrics reported in Table I. We also measured inter-reader reliability on DIP and PIP joints for a subset of radiographs from 70 participants not in the training set that were evaluated by Readers 1 (IKH) and 2 (SMB) and the DL model. The Pearson’s r-value from a least-squares fit comparing the sums across all multiclass scores was used. Reader 1 data were used to train the models.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;On the test sets, marker detection achieved an mAP50 of 0.98 and a macro F1-score of 0.97, while laterality detection achieved 0.99 for both metrics. Keypoint localization achieved an SDR@2mm of 0.95. For OARSI classification, macro F1-scores were 0.88 and 0.89 for binary JSN and OST models, respectively, and 0.78 and 0.72 for multiclass JSN and OST models, respectively. The reader reliability results were as follows: Reader 1 vs. Reader 2: r=0.89, Rea","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100412"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148543597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sponsors","authors":"","doi":"10.1016/S2772-6541(26)00072-3","DOIUrl":"10.1016/S2772-6541(26)00072-3","url":null,"abstract":"","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"6 ","pages":"Article 100462"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148543632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}