Osteoarthritis imaging最新文献

{"title":"Longitudinal MRI-defined cartilage loss and radiographic joint space narrowing following intra-articular corticosteroid injection for knee osteoarthritis: A systematic review and meta-analysis","authors":"Hamza Ahmed Ibad ,&nbsp;Arta Kasaeian ,&nbsp;Elena Ghotbi ,&nbsp;Frank Roemer ,&nbsp;Mohamed Jarraya ,&nbsp;Farzaneh Ghazi-Sherbaf ,&nbsp;Mahsa Dolatshahi ,&nbsp;Shadpour Demehri ,&nbsp;Ali Guermazi","doi":"10.1016/j.ostima.2023.100157","DOIUrl":"10.1016/j.ostima.2023.100157","url":null,"abstract":"<div><h3>Background</h3><p>Intra-articular corticosteroid injections (IACS) are interventions which provide pain relief in knee osteoarthritis (OA). It remains unclear whether IACS have a deleterious effect on knee cartilage structure.</p></div><div><h3>Purpose</h3><p>To estimate the effect of IACS on cartilage structure in patients with knee OA, using joint space width (JSW) (in radiographic studies), and cartilage thickness (in magnetic resonance imaging).</p></div><div><h3>Materials and methods</h3><p>A literature search was performed to identify randomized control trials and observational studies published from inception to June 15, 2022. Studies were included if patients received IACS for knee OA, with a control arm. Given the different metrics used in reporting continuous variable outcomes among studies, pooled estimates for cartilage thickness change were assessed using standardized mean differences (defined as the difference between the means of the groups divided by a within-group standard deviation) to odds ratio transformation. Sensitivity analyses were conducted based on outcome metric, imaging modality, and number of injections.</p></div><div><h3>Results</h3><p>Six studies (1437 participants) were identified. The estimated effect of IACS on cartilage structure revealed greater odds of cartilage structure worsening (Odds Ratio (OR): 2.01, 95% Confidence Interval (CI): 1.18,3.44). Sensitivity analyses revealed similar trends, with significant results for singular injections with preference to JSW (OR: 2.44, 95%CI: 1.23,4.82), radiographic outcomes with preference to KL grade (OR: 2.03, 95%CI: 1.01,4.10), binary outcomes with preference to KL grade (OR: 2.93, 95%CI: 1.18,7.25) and quantitative measures (Standardized Mean Differences (SMD): -0.34, 95%CI: -0.66, -0.02)</p></div><div><h3>Conclusions</h3><p>IACS use may contribute to imaging features of knee cartilage loss. Further studies are warranted to investigate the underlying pathogenesis.</p></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"3 3","pages":"Article 100157"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46331991","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":"Reliability of ultrasound-detected effusion-synovitis in knee osteoarthritis","authors":"Lindsey A. MacFarlane ,&nbsp;Maame B. Opare-Addo ,&nbsp;Jeffrey N. Katz ,&nbsp;Jamie E. Collins ,&nbsp;Elena Losina ,&nbsp;Sara K. Tedeschi","doi":"10.1016/j.ostima.2023.100164","DOIUrl":"10.1016/j.ostima.2023.100164","url":null,"abstract":"<div><h3>Objective</h3><p>There is increasing use of ultrasound to identify effusion and synovitis, which are proxies for inflammation, in knee osteoarthritis (OA). Ultrasound appears to be a valid modality for assessing effusion and synovitis when compared to MRI, but its reliability has been questioned. We aimed to assess the agreement of ultrasound-identified effusion and synovitis between two readers.</p></div><div><h3>Design</h3><p>We included 30 unique knees from a cohort with symptomatic and radiographic knee OA. A single ultrasonographer performed ultrasound of the suprapatellar recess. Two readers with different levels of experience read the images. We specified maximal effusion depth first as continuous (mm) and then as binary, dichotomized at ≥4 mm (present) vs. &lt;4 mm (absent). We graded synovitis in the suprapatellar recess (0–3) based on the Outcome Measures in Rheumatology (OMERACT) guidelines. We used proportions to determine inter-rater agreement for continuous variables and Cohen's Kappa or weighted Cohen's Kappa for agreement of binary and ordinal variables.</p></div><div><h3>Results</h3><p>The mean age of the participants was 66 years. Seventeen (57%) were female. For maximal effusion depth the readers differed by ≤ 2 mm 80% of the time. For the presence of effusion (binary), the Cohen's Kappa for the two readers was 0.69 (95% CI 0.40–0.97). Synovitis grade (0–3) for the two readers had a weighted Cohen's Kappa of 0.76 (95% CI 0.58–0.94).</p></div><div><h3>Conclusion</h3><p>We found moderate to strong inter-reader agreement of ultrasound identified effusion-synovitis in knee OA between two readers with differing ultrasound experience. Ultrasound may be a viable bedside tool for identifying the subset of OA patients with effusion or synovitis.</p></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"3 3","pages":"Article 100164"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44532346","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":"Diagnostic performance for severity grading of hip osteoarthritis and osteonecrosis of femoral head on radiographs: Deep learning model vs. board-certified orthopaedic surgeons","authors":"Chen Chen ,&nbsp;Peng Liu ,&nbsp;Yong Feng ,&nbsp;DeXian Ye ,&nbsp;Chi-Cheng Fu ,&nbsp;Lin Ye ,&nbsp;YanYan Song ,&nbsp;DongXu Liu ,&nbsp;Guoyan Zheng ,&nbsp;ChangQing Zhang","doi":"10.1016/j.ostima.2023.100092","DOIUrl":"10.1016/j.ostima.2023.100092","url":null,"abstract":"<div><h3>Objectives</h3><p>To evaluate the diagnostic performance of a single deep learning (DL) model for severity grading of two typical yet challenging hip disorders, primary hip osteoarthritis (PHOA) and osteonecrosis of the femoral head (ONFH), on digital radiography.</p></div><div><h3>Design</h3><p>We conducted a two-center, retrospective study. We trained an XceptionNet-based DL model using a dataset consisting of 56,597 hip images diagnosed as normal, PHOA_I, PHOA_II, PHOA_III, and ONFH_II, ONFH_III, ONFH_IV by a panel of 10 board-certified orthopedic surgeons. The trained model was validated on a separate testing dataset. To demonstrate the model's generalizability, we applied the trained model directly to a dataset consisting of 811 hip images collected from an external clinical center.</p></div><div><h3>Results</h3><p>Accuracy, area under the curve (AUC) of receiver operating characteristics, sensitivity, and specificity were investigated. Validated on the testing dataset, the model achieved an overall AUC of 94.9%, with individual AUC scores of 94.2% for PHOA_I, 95.8% for PHOA_II, 90.9% for PHOA_III, 93.6% for ONFH_II, 93.8% for ONFH_III, and 93.8% for ONFH_IV. The average sensitivity for all classes of the DL algorithm (0.797) was better than the average level of the board-certified orthopedic surgeons (0.756). When applied directly to the external dataset, the AUC of the trained model is degraded.</p></div><div><h3>Conclusion</h3><p>We can train a single DL model to grade the severity of PHOA and ONFH on digital radiographs. The model may be used to provide a second opinion for severity grading of hip disorders on digital radiographs.</p></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"3 2","pages":"Article 100092"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48724985","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":"Agreement and accuracy of fully automated morphometric femorotibial cartilage analysis in radiographic knee osteoarthritis","authors":"Felix Eckstein ,&nbsp;Akshay S. Chaudhari ,&nbsp;Jana Kemnitz ,&nbsp;Christian F. Baumgartner ,&nbsp;Wolfgang Wirth","doi":"10.1016/j.ostima.2023.100156","DOIUrl":"10.1016/j.ostima.2023.100156","url":null,"abstract":"<div><h3>Objective</h3><p>To examine the performance of automated convolutional neuronal network cartilage segmentation in knees with radiographic osteoarthritis (ROA), and its dependence on the OA status of the training set and MRI sequences.</p></div><div><h3>Design</h3><p>We studied 122 ROA and 92 healthy reference cohort (HRC) knees from the Osteoarthritis Initiative. All knees had expert manual segmentation of the femorotibial cartilages based on coronal FLASH and sagittal DESS MRI. Two U-net convolutional neural networks were trained on 86/50 ROA/HRC knees, validated on 18/21, and tested on 18/21.</p></div><div><h3>Results</h3><p>Of 122 ROA knees, 43 (35%) were KLG2, 41 (34%) KLG3, and 38 (31%) KLG4. In the ROA test set, Dice Similarity Coefficients (DSCs) were 0.86/0.86 (FLASH/DESS) for the algorithm trained on ROA, and 0.82/0.82 for that trained on HRC knees. In the HRC test set, mean DSCs of 0.91/0.90 were observed with FLASH/DESS, both with the HRC- and with the ROA-trained algorithm. Cartilage thickness computation from automated segmentation in the FLASH ROA test set showed a correlation of <em>r</em> = 0.94 with manual segmentation for the ROA-trained, and of <em>r</em> = 0.89 for the HRC-trained algorithm. In the FLASH HRC test set, it exhibited a correlation of <em>r</em> = 0.96 for the HRC-trained and <em>r</em> = 0.88 for the ROA-trained algorithm. Results were similar between the DESS and FLASH, but were less accurate for KLG4 than for KLG2/3 knees.</p></div><div><h3>Conclusions</h3><p>An automated algorithm trained on ROA knees was able to accurately segment and compute cartilage thickness on FLASH and DESS MRI, in both ROA and healthy knees, whereas an algorithm trained on healthy knees did not perform as well in ROA knees.</p></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"3 2","pages":"Article 100156"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46680416","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":"Supplemental educational program to heighten the impact of research – an opportunity for OA imaging","authors":"Deborah Burstein ,&nbsp;Rudolph Mitchell ,&nbsp;Emery Brown ,&nbsp;Martha L. Gray","doi":"10.1016/j.ostima.2023.100155","DOIUrl":"10.1016/j.ostima.2023.100155","url":null,"abstract":"<div><h3>Objective</h3><p>To develop and evaluate a supplementary educational program (“IMPACT”) centered on enabling participants to consider specifically and articulate explicitly the best path for and potential impact of their research.</p></div><div><h3>Design</h3><p>Participants (trainees) and faculty mentors were from all areas of biomedical research. The group worked longitudinally in small, rotating groups, through a process of developing a written statement (“Impact Statement”), an overview (“Impact Storyline”) and an oral presentation (“Impact Case”) of their work.</p></div><div><h3>Results</h3><p>One hundred and eighty-seven Fellows enrolled in the program. Of the 179 (96%) Fellows who completed the program, 159 (89%) responded to a post-program survey; 94% indicated that IMPACT was a significant learning experience, 89% indicated that they were more able to identify the long-term potential of their research, 95% felt more able to talk about their work to diverse audiences.</p></div><div><h3>Conclusion</h3><p>This voluntary educational program was appreciated by the participants and led to increased confidence in their ability to drive their science towards a clear impact and communicating that potential to others. This type of program may aid in redirecting some of the efforts and resources of imaging in OA from the large focus on technical developments to more direct biological and clinical questions which might be resolved with current technology.</p></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"3 2","pages":"Article 100155"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/12/e5/nihms-1933628.PMC10552445.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41175422","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":"Hybrid single-photon emission computed tomography bone imaging for the evaluation of non-specific lower back pain","authors":"Sayed Samed Talibi BSc (Hons), MBChB, MRCS (Ed), MBA ,&nbsp;Lucas Rakasz MBBS, FRCS (SN) ,&nbsp;James Hodson BSc (Hons) ,&nbsp;Jasmeet Dhir MBBS, FRCS (SN) ,&nbsp;Satheesh Ramalingam MBBS, FRCR","doi":"10.1016/j.ostima.2023.100086","DOIUrl":"10.1016/j.ostima.2023.100086","url":null,"abstract":"<div><h3>Background</h3><p>This is a single-center retrospective study designed to assess the correlation between the location of “hots spots” on single-photon emission computed tomography (SPECT) and the severity of joint degeneration on computed tomography (CT), in addition to understanding whether these hot spots correlate with the pain generating sites causing the non-specific lower back pain.</p></div><div><h3>Methods</h3><p>All patients who had undergone hybrid SPECT-CT imaging of the lower back over a one-year period at our institution were identified. Twenty joints were assessed from each patient. Joints for assessment were chosen from the intervertebral discs, facet (zygapophysial) and sacroiliac joints. Diagnostic accuracy was assessed using receiver operating characteristic (ROC) curves and quantified using the area under the receiver operating characteristic curve (AUROC).</p></div><div><h3>Results</h3><p>Over a one-year period, 111 patients were identified, with the primary indication being non-specific lower back pain in 73 (66%). Hot spots were observed on SPECT in 79% of patients, with 86% having some degree of degeneration in at least one joint on CT. Degeneration was found to be significantly associated with the presence of hot spots for the majority of joints, with the strongest association seen in the L3/L4 intervertebral joint, for which the rates of hot spots were 1% for grade 0, 26% for grade 1 and 78% for grades 2–3 (AUROC: 0.91, <em>p</em>&lt;0.001). Neither the presence of hot spots nor degeneration were found to be significantly predictive of non-specific lower back pain for any of the joints considered.</p></div><div><h3>Conclusions</h3><p>Hot spots identified on SPECT are correlated with the presence of degeneration on CT but have limited diagnostic ability to identify potential causes of non-specific lower back pain.</p></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"3 1","pages":"Article 100086"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42860264","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":"Towards automatic cartilage quantification in clinical trials – Continuing from the 2019 IWOAI knee segmentation challenge","authors":"Erik B Dam ,&nbsp;Arjun D Desai ,&nbsp;Cem M Deniz ,&nbsp;Haresh R Rajamohan ,&nbsp;Ravinder Regatte ,&nbsp;Claudia Iriondo ,&nbsp;Valentina Pedoia ,&nbsp;Sharmila Majumdar ,&nbsp;Mathias Perslev ,&nbsp;Christian Igel ,&nbsp;Akshay Pai ,&nbsp;Sibaji Gaj ,&nbsp;Mingrui Yang ,&nbsp;Kunio Nakamura ,&nbsp;Xiaojuan Li ,&nbsp;Hasan Maqbool ,&nbsp;Ismail Irmakci ,&nbsp;Sang-Eun Song ,&nbsp;Ulas Bagci ,&nbsp;Brian Hargreaves ,&nbsp;Akshay Chaudhari","doi":"10.1016/j.ostima.2023.100087","DOIUrl":"10.1016/j.ostima.2023.100087","url":null,"abstract":"<div><h3>Objective</h3><p>To evaluate whether the deep learning (DL) segmentation methods from the six teams that participated in the IWOAI 2019 Knee Cartilage Segmentation Challenge are appropriate for quantifying cartilage loss in longitudinal clinical trials.</p></div><div><h3>Design</h3><p>We included 556 subjects from the Osteoarthritis Initiative study with manually read cartilage volume scores for the baseline and 1-year visits. The teams used their methods originally trained for the IWOAI 2019 challenge to segment the 1130 knee MRIs. These scans were anonymized and the teams were blinded to any subject or visit identifiers. Two teams also submitted updated methods. The resulting 9,040 segmentations are available online.</p><p>The segmentations included tibial, femoral, and patellar compartments. In post-processing, we extracted medial and lateral tibial compartments and geometrically defined central medial and lateral femoral sub-compartments. The primary study outcome was the sensitivity to measure cartilage loss as defined by the standardized response mean (SRM).</p></div><div><h3>Results</h3><p>For the tibial compartments, several of the DL segmentation methods had SRMs similar to the gold standard manual method. The highest DL SRM was for the lateral tibial compartment at 0.38 (the gold standard had 0.34). For the femoral compartments, the gold standard had higher SRMs than the automatic methods at 0.31/0.30 for medial/lateral compartments.</p></div><div><h3>Conclusion</h3><p>The lower SRMs for the DL methods in the femoral compartments at 0.2 were possibly due to the simple sub-compartment extraction done during post-processing. The study demonstrated that state-of-the-art DL segmentation methods may be used in standardized longitudinal single-scanner clinical trials for well-defined cartilage compartments.</p></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"3 1","pages":"Article 100087"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47230716","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":"Quantitative morphometric analysis in tibiofemoral joint osteoarthritis imaging: A literature review","authors":"Pholpat Durongbhan ,&nbsp;James W. MacKay ,&nbsp;Jemima E. Schadow ,&nbsp;Catherine E. Davey ,&nbsp;Kathryn S. Stok","doi":"10.1016/j.ostima.2023.100088","DOIUrl":"10.1016/j.ostima.2023.100088","url":null,"abstract":"<div><h3>Objective</h3><p>To highlight published quantitative morphometric analysis (QMA) methods for assessing tibiofemoral joint osteoarthritis in magnetic resonance imaging and computed tomography and underline the need for open and interoperable protocols for quantitative image analysis.</p></div><div><h3>Design</h3><p>A literature search on PubMed/MEDLINE and Web of Science of keywords relating to QMA in magnetic resonance imaging and computed tomography in the context of tibiofemoral osteoarthritis in the past 23 years (2000–2022). The search was based on, but not limited to: “quantitative morphometric analysis” or “quantitative image analysis” in combination with “osteoarthritis”, “articular cartilage”, “subchondral bone”, “tibiofemoral joint”, “joint”, “CT”, and “MRI”. The search found 73 relevant publications that were manually screened and sorted for QMA methods. A further search to extract key functions of the underlying algorithms was performed.</p></div><div><h3>Result</h3><p>MRI is generally used for QMA of articular cartilage and joint contact area, while CT is generally used for QMA of subchondral bone and joint space width. Studies have shown that QMA algorithms can be adapted to new tissues and modalities. However, many methods are not easily accessible, and there is fragmentation of computational tools and platforms in the research field.</p></div><div><h3>Conclusion</h3><p>QMA is an active research area, and many techniques from one modality can be readily extended to another. Adoption of open-source practices can allow algorithms developed for other imaging modalities to be shared, making it possible to bridge the knowledge gap for structures and pathological features for which QMA has not yet been investigated and to increase research output overall.</p></div>","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"3 1","pages":"Article 100088"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45692245","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":"DYNAMIC CONTRAST-ENHANCED MRI OF THE SYNOVIUM IN KNEE OSTEOARTHRITIS: SEMI-AUTOMATIC SEGMENTATION OF SYNOVIAL SUBREGIONS AND TEST-RETEST REPEATABILITY","authors":"J.M. Mostert ,&nbsp;T.A. Van Zadelhoff ,&nbsp;D.H.J. Poot ,&nbsp;D. Van der Kaaij ,&nbsp;L. Strong ,&nbsp;K. Zijlstra ,&nbsp;E.H.G. Oei ,&nbsp;R.A. Van der Heijden","doi":"10.1016/j.ostima.2023.100135","DOIUrl":"https://doi.org/10.1016/j.ostima.2023.100135","url":null,"abstract":"","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"3 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49773787","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":"PREDICTING TROCHLEAR DYSPLASIA FROM 3-D CORTICAL BONE AND SHAPE PARAMETERS","authors":"S. Brinch ,&nbsp;P. Hansen ,&nbsp;F.H. Linden ,&nbsp;M. Krogsgaard ,&nbsp;P. Lavard ,&nbsp;M. Boesen ,&nbsp;K.E.S. Poole ,&nbsp;T.D. Turmezei","doi":"10.1016/j.ostima.2023.100100","DOIUrl":"https://doi.org/10.1016/j.ostima.2023.100100","url":null,"abstract":"","PeriodicalId":74378,"journal":{"name":"Osteoarthritis imaging","volume":"3 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49786710","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}