Knee最新文献

{"title":"Can a machine learning approach contribute to monitoring post-market surveillance of total knee arthroplasty prostheses?","authors":"Khashayar Ghadirinejad ,&nbsp;Stephen Graves ,&nbsp;Richard de Steiger ,&nbsp;Nicole Pratt ,&nbsp;Lucian B. Solomon ,&nbsp;Mark Taylor ,&nbsp;Reza Hashemi","doi":"10.1016/j.knee.2025.10.020","DOIUrl":"10.1016/j.knee.2025.10.020","url":null,"abstract":"<div><h3>Background</h3><div>The impact of knee replacement surgery on patients can be monitored by joint registries through the surveillance of prostheses and identification of under-performing devices – outliers. The study of new statistical methods can help determine whether a device is at a higher risk of failure by considering possible confounding factors. Self-learning algorithms with the potential to involve multiple variables simultaneously are one approach to limiting the impact of confounding factors. This study aimed to assess two machine learning (ML) techniques to detect total knee outliers while controlling for patient- and device-related confounding.</div></div><div><h3>Methods</h3><div>The potential to identify outliers among 160 unique prostheses was evaluated for Random Survival Forest (RSF) and regularised/unregularised Cox models. The input variables included femoral/tibial components and patient characteristics provided by the Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR) on 265,655 primary total knee procedures. The outcome was time to first revision surgery treated as a censored case for death, and the AOANJRR gold standard was defined as the criteria to assess the effectiveness of proposed ML methods.</div></div><div><h3>Results</h3><div>In the study cohort, the AOANJRR standardised approach detected five conventional prosthesis combinations. Both the Cox and RSF techniques identified two of the same total knee prostheses. The regularised/unregularised Cox results were more comparable to the AOANJRR standard by detecting one additional prosthesis at a higher risk of revision.</div></div><div><h3>Conclusion</h3><div>Machine learning may offer a supplementary approach for the identification of prosthesis outliers. However, further analysis is required to fully comprehend the effect of confounding factors and the potential contribution of ML to the early identification of outliers.</div></div>","PeriodicalId":56110,"journal":{"name":"Knee","volume":"58 ","pages":"Article 104266"},"PeriodicalIF":2.0,"publicationDate":"2025-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145535037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reevaluating periarticular injection for pain control in total knee arthroplasty","authors":"Tien-Yu Fu,&nbsp;Chia-Hao Hsu","doi":"10.1016/j.knee.2025.10.018","DOIUrl":"10.1016/j.knee.2025.10.018","url":null,"abstract":"","PeriodicalId":56110,"journal":{"name":"Knee","volume":"58 ","pages":"Article 104264"},"PeriodicalIF":2.0,"publicationDate":"2025-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145534985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-enabled segmentation of knee cartilage in conventional magnetic resonance images: Internal and external validation of different models","authors":"Lin Yao ,&nbsp;Xiaoling Liang ,&nbsp;Jin Liu ,&nbsp;Baoxin Qian ,&nbsp;Zhongli Xiao ,&nbsp;Dantian Zhu ,&nbsp;Jiaxin Feng ,&nbsp;Shouguo Zhou ,&nbsp;Huaqian Cui ,&nbsp;Shaolin Li ,&nbsp;Wei Li","doi":"10.1016/j.knee.2025.10.024","DOIUrl":"10.1016/j.knee.2025.10.024","url":null,"abstract":"<div><h3>Background</h3><div>Accurate evaluation of the cartilage anatomy of the knee is helpful for clinical evaluation of the source of knee pain and the classification and treatment of knee osteoarthritis (OA). This study proposes a deep learning model for segmentation of knee articular cartilage in conventional proton density fat-saturated MRI sequences to assess cartilage morphology for subsequent injury grading.</div></div><div><h3>Methods</h3><div>This retrospective study was conducted at two radiology centers, involving 254 knees from 254 patients who had previously undergone MRI scans. The training-internal validation cohort included 219 knees from Center 1. The external validation cohort comprised 35 knees from Center 2. Two musculoskeletal radiology experts manually annotated the cartilage regions. A 3D Res U-net model was employed for segmentation, and its performance was compared with 3D U-net and 3D V-net models. Segmentation results were evaluated using the Dice coefficient and Jaccard index.</div></div><div><h3>Results</h3><div>The 3D Res U-net model demonstrated superior segmentation performance compared to the other deep learning methods. For cartilage in the lateral femorotibial joint, medial femorotibial joint, and patellofemoral joint, the average Dice coefficients with 3D Res U-net were 0.871, 0.860, and 0.858 in internal validation and 0.846, 0.837, and 0.819 in external validation, respectively. The Jaccard index followed a similar trend.</div></div><div><h3>Conclusion</h3><div>The 3D Res U-net model improves knee cartilage segmentation in conventional MR imaging, contributing to the understanding of cartilage morphology and the improvement of clinically relevant decisions.</div></div>","PeriodicalId":56110,"journal":{"name":"Knee","volume":"58 ","pages":"Article 104270"},"PeriodicalIF":2.0,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145530716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association of preoperative GLP‑1 receptor agonist use with outcomes after primary total knee arthroplasty","authors":"McKenna W. Box ,&nbsp;Troy B. Puga ,&nbsp;Neil J. Werthmann ,&nbsp;Andrew Jen ,&nbsp;Yingxian Liu ,&nbsp;John T. Riehl","doi":"10.1016/j.knee.2025.10.026","DOIUrl":"10.1016/j.knee.2025.10.026","url":null,"abstract":"<div><h3>Background</h3><div>Evidence on whether glucagon-like peptide 1 receptor agonists (GLP-1RA) influence outcomes after total knee arthroplasty (TKA) is mixed. We evaluated the association between perioperative use of GLP-1RA and postoperative outcomes in patients with and without diabetes.</div></div><div><h3>Methods</h3><div>In this retrospective cohort from a single U.S. health system (2019–2023), adults undergoing primary TKA were stratified by GLP‑1RA use at the time of surgery. Primary outcomes included length-of-stay (LOS), 30-day medical complications, 90-day readmissions and surgical site infection (SSI), 1-year SSI, medical complications, and TKA implant complications, and revision TKA. Multivariable logistic regression and negative binomial regression were adjusted for age group, sex, BMI, smoking status, diabetes, and the Elixhauser Comorbidity Index. Unadjusted subgroup analyses examined outcomes by diabetes status and specific GLP-1RA agent.</div></div><div><h3>Results</h3><div>Among 26,154 TKA patients, 914 (3.5 %) used a GLP-1RA (73 % of these had diabetes). GLP-1RA use was associated with 45 % lower odds of 1-year implant complications (odds ratio = 0.55, 95 % CI 0.33–0.90, <em>P</em> = 0.02) and 10 % shorter LOS (incidence rate ratio = 0.90, 95 % CI 0.85–0.94, <em>P</em> &lt; 0.001) (absolute difference of 0.17 days). No significant differences in 30-day complications, 90-day SSI, or readmissions were observed after adjustment.</div></div><div><h3>Conclusions</h3><div>Preoperative GLP-1RA use was associated with reduced one-year implant complications and slightly shorter hospital stays following TKA. These findings, although encouraging, are exploratory. Larger, adjusted analyses are needed to confirm the benefits before recommending changes in perioperative management.</div></div>","PeriodicalId":56110,"journal":{"name":"Knee","volume":"58 ","pages":"Article 104272"},"PeriodicalIF":2.0,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145530706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic detection of knee medial collateral ligament (MCL) tear from magnetic resonance imaging using deep neural network","authors":"Elahe Mirzakhani ,&nbsp;Mohammad Ayati Firoozabadi ,&nbsp;Mohammadreza Razzaghof ,&nbsp;Seyed Mohammad Javad Mortazavi ,&nbsp;Toktam Khatibi","doi":"10.1016/j.knee.2025.10.028","DOIUrl":"10.1016/j.knee.2025.10.028","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;The medial collateral ligament (MCL) is a crucial structure supporting the stability of the knee joint. Although a clinical examination can detect an MCL tear, valgus stress radiography and magnetic resonance imaging (MRI) can confirm it. However, challenges persist in accurate MCL tear detection from MRI images, often leading to misdiagnosis and treatment delays. Therefore, proposing automatic methods for detecting MCL tears is necessary. To the best of the researcher’s knowledge, studies have yet to address this problem using deep neural networks.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Method&lt;/h3&gt;&lt;div&gt;This study aims to detect medial collateral ligament (MCL) tears through knee MRI images. Our collected dataset includes coronal views of knee MRI images taken from patients in “Imam Khomeini Hospital, Tehran, Iran.” The dataset has 3575 knee MRI images from 60 patients, each with a resolution of 512 × 512 pixels. We employ the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology to structure our approach, which includes three distinct scenarios utilizing deep learning models. In Scenario One, A custom Convolutional Neural Network (CNN) architecture is designed specifically for MCL tear detection. This model undergoes a meticulous fine-tuning process and is evaluated using a comprehensive knee MRI dataset. We adapt the hyperparameters of the CNN to accurate optimize its performance, ensuring classification of MCL tears. In the second scenario, we leverage a deep neural network pre-trained on the ImageNet dataset. The pre-trained VGG19 model is utilized, where we extract features from its layers and feed them into custom output layer to classify MCL tears using fine tuning. This approach allows us to assess the effectiveness of transfer learning in improving MCL tear detection. In the third scenario, we implement transfer learning using the VGG19 architecture. We apply transfer learning by freezing the early layers of the pre-trained VGG19 model and modifying its final layers. This innovative approach aims to enhance the model’s ability to accurately identify MCL tears by utilizing learned features from a large dataset.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Result&lt;/h3&gt;&lt;div&gt;The experimental results demonstrate that the first scenario achieved an accuracy of 95 %. The second scenario outperformed the others, achieving an average accuracy of 98.3 %, an average loss of 0.07, and an area under the receiver operating characteristic (ROC) curve (AUC) of 1.00. In contrast, the third scenario attained an accuracy of 80 %.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Discussion&lt;/h3&gt;&lt;div&gt;This study highlights the effectiveness of deep learning, particularly the pre-trained VGG19 model, in detecting MCL tears from knee MRI images with 98.3 % accuracy. By leveraging transfer learning, our approach mitigates data limitations, demonstrating the potential of automated diagnostic tools to improve accuracy and efficiency in clinical practice.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusions&lt;/h3&gt;&lt;d","PeriodicalId":56110,"journal":{"name":"Knee","volume":"58 ","pages":"Article 104274"},"PeriodicalIF":2.0,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145524895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The use of machine learning in predicting anterior cruciate ligament injury: a systematic review and meta-analysis","authors":"Longfei Guo ,&nbsp;Zhilei Cui ,&nbsp;Wei Ping Loh ,&nbsp;Shazlin Shaharudin","doi":"10.1016/j.knee.2025.10.021","DOIUrl":"10.1016/j.knee.2025.10.021","url":null,"abstract":"<div><h3>Background</h3><div>Machine learning (ML) models are used to analyze the relationship between risk factors and anterior cruciate ligament (ACL) injury outcomes. This review assessed the practicality of ACL injury prediction models, analyzed the existing issues, and provided valuable information for future research.</div></div><div><h3>Methods</h3><div>A comprehensive search was conducted across PubMed, Medline, Embase, Scopus, Web of Science, and Cochrane databases for relevant studies until 30th April 2024.</div></div><div><h3>Results</h3><div>The search yielded 633 studies, of which eight articles (32 predictive models) were included in the final evaluation. Based on the results, the <em>meta</em>-analysis quantification yielded an overall area under the curve (AUC) of 0.79 (95 % CI: 0.75–0.82), a combined sensitivity of 0.57 (95 % CI: 0.45–0.68), and a combined specificity of 0.87 (95 % CI: 0.78–0.92). The included models comprised 10 ensemble algorithms and 22 non-ensemble algorithms. Ensemble methods demonstrated higher specificity (0.96 vs. 0.79) and AUC (0.79 vs. 0.68), whereas non-ensemble models showed better sensitivity (0.65 vs. 0.40).</div></div><div><h3>Conclusion</h3><div>ML models were effective in correctly identifying non-injury cases, however their ability to detect actual injury occurrences required significant improvement. Algorithm selection significantly influenced performance trade-offs: ensemble models favored specificity, whereas non-ensemble models provided superior sensitivity. These findings may guide algorithm selection to improve the accuracy and efficiency of injury prediction tools in sports medicine. Despite the challenges posed by the diversity of injury mechanisms, the study emphasizes the importance of high-quality biomechanical data, prospective study designs, and standardized methodologies in enhancing model reliability and clinical applicability, providing a basis for optimizing ACL injury prediction models.</div></div>","PeriodicalId":56110,"journal":{"name":"Knee","volume":"58 ","pages":"Article 104267"},"PeriodicalIF":2.0,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145515166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative performance of the Risk Analysis Index versus traditional frailty measures in predicting outcomes following unicompartmental knee arthroplasty: A national database analysis","authors":"Cameron J. Sabet ,&nbsp;Bhav Jain ,&nbsp;Jad Lawand ,&nbsp;Stefan Prulovic ,&nbsp;Bill Young ,&nbsp;Dang Nguyen ,&nbsp;Bara M. Hammadeh ,&nbsp;Jiaqi Liu","doi":"10.1016/j.knee.2025.10.015","DOIUrl":"10.1016/j.knee.2025.10.015","url":null,"abstract":"<div><h3>Background</h3><div>While multiple frailty assessment tools exist for perioperative risk stratification, their comparative effectiveness in unicompartmental knee arthroplasty (UKA) remains unclear. The Risk Analysis Index (RAI) represents a comprehensive frailty measure, but its performance relative to established indices requires validation. We aimed to compare the predictive accuracy of the RAI against traditional frailty measures including the modified Frailty Index-5 (mFI-5) and Geriatric Nutritional Risk Index (GNRI) for 30-day outcomes following UKA.</div></div><div><h3>Methods</h3><div>We analyzed 9358 patients undergoing elective UKA from the ACS-NSQIP database (2015–2021). Three frailty indices were calculated: RAI (incorporating age, sex, renal function, dyspnea, cancer, weight loss, and functional status), mFI-5 (five comorbidity domains), and GNRI (nutritional assessment). Primary outcomes were discharge disposition and 30-day readmission. Secondary outcomes included 30-day all-cause mortality, complications, reoperations, and extended length of stay.</div></div><div><h3>Results</h3><div>The RAI demonstrated superior discrimination for discharge disposition (area under the curve (AUC) = 0.694) and 30-day readmission (AUC = 0.615) compared with mFI-5 (AUC = 0.593 and 0.570) and GNRI (AUC = 0.521 and 0.558). Progressive increases in adverse outcomes occurred across RAI tiers, with non-home discharge rates of 1.7 % in robust patients versus 15.9 % in frail patients (<em>P</em> &lt; 0.001).</div></div><div><h3>Conclusions</h3><div>The RAI provides superior risk stratification compared with traditional frailty measures for discharge disposition and 30-day readmission in particular following UKA, supporting its adoption as the preferred perioperative assessment tool.</div></div>","PeriodicalId":56110,"journal":{"name":"Knee","volume":"58 ","pages":"Article 104261"},"PeriodicalIF":2.0,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145497567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knee joint biomechanics under external focus instructions promoting a quiet, safe and soft landing","authors":"Lukáš Slovák ,&nbsp;David Zahradník ,&nbsp;William M. Land ,&nbsp;Javad Sarvestan ,&nbsp;Takehiro Iwatsuki ,&nbsp;Kevin A. Becker ,&nbsp;Reza Abdollahipour","doi":"10.1016/j.knee.2025.10.013","DOIUrl":"10.1016/j.knee.2025.10.013","url":null,"abstract":"<div><h3>Background</h3><div>Anterior cruciate ligament injuries are common in young female athletes, often occurring during landing tasks. Attentional focus instructions may influence landing biomechanics and reduce injury risk, but comparative effects of different external focus (EF) cues remain unclear.</div></div><div><h3>Methods</h3><div>Twelve novice female volleyball players (age: 13.6 ± 0.6 years) performed double-leg drop landings from a height of 50  cm under four attentional focus conditions: (1) as quietly as possible, (2) as safely as possible, (3) as softly as possible, and (4) a no-focus control. A Qualisys 3D motion capture system and a Kistler force platform were used to analyze knee joint kinematics and kinetics during initial contact and the first two VGRF peaks, and time-series analysis was conducted to characterize the biomechanical landing pattern across conditions.</div></div><div><h3>Results</h3><div>All EF conditions significantly reduced biomechanical knee joint loading compared with the no-focus condition. Although no significant differences were found among the three EF instructions, the ’soft’ landing instruction produced the most pronounced changes, showing the lowest maximal VGRF, increased knee flexion angles, and reduced internal rotation angles, relative to the no-focus condition.</div></div><div><h3>Conclusion</h3><div>EF instructions, particularly those emphasizing a soft landing, can effectively reduce knee joint loading and potentially lower anterior cruciate ligament injury risk in young female athletes. These findings highlight the value of incorporating specific attentional focus cues into injury prevention and training programs.</div></div>","PeriodicalId":56110,"journal":{"name":"Knee","volume":"58 ","pages":"Article 104259"},"PeriodicalIF":2.0,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145497478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patellotrochlear index: A systematic review of the first 20 years of application","authors":"Roland Manfred Biedert","doi":"10.1016/j.knee.2025.10.017","DOIUrl":"10.1016/j.knee.2025.10.017","url":null,"abstract":"<div><h3>Objective</h3><div>To provide an update on the reliability and application of the patellotrochlear index (PTI) used to assess patellar height (PH) measurement.</div></div><div><h3>Methods</h3><div>A systematic literature search was conducted through PubMed registries in 2024 using the terms PTI, PH, and MRI. Inclusion criteria were original studies or review articles from 2006 onwards. All evidence levels were included. Reliability, cut-off values, statistics, and influence of muscle contraction, flexion, and weight bearing on the PTI were assessed. Comparisons with conventional radiographic measurement methods on MRI were evaluated.</div></div><div><h3>Results</h3><div>Twenty-four studies met the selection criteria. These studies used a direct measure of the patellotrochlear relationship for PH on sagittal MRI, 12 used the original PTI. Five studies evaluated the effect of quadriceps contraction, weight bearing, or flexion on the PTI values. For healthy subjects, the normal PTI values varied between 31.7 % and 36.8 %, for patella alta they were &lt;12.5 % and varied for patella infera between &gt;50 % and 80 %. In symptomatic subjects, the normal PTI values varied between &lt;11.9 % and 49 %, for patella alta &lt;11.9 % and 18 %, and for patella infera &gt;50 % and 80 % depending on the pathology. Only weak or no correlation between the PTI and measurements on conventional radiography was revealed. The statistical method influenced the PTI values.</div></div><div><h3>Conclusion</h3><div>The PTI is a reliable diagnostic tool for PH measurement on MRI. PTI values described in the studies are different for the various pathologies. Advantages and limitations exist. Radiographic measurement methods applied on MRI are not suitable.</div></div>","PeriodicalId":56110,"journal":{"name":"Knee","volume":"58 ","pages":"Article 104263"},"PeriodicalIF":2.0,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145497510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of meniscal root tears on knee osteoarthritis development: A systematic review of the literature","authors":"Gianluca Canton ,&nbsp;Andrea Marchetti ,&nbsp;Belinda Trobec ,&nbsp;Michelangelo Scordo ,&nbsp;Alessandra Mazzon ,&nbsp;Chiara Ratti ,&nbsp;Manuela Deodato ,&nbsp;Alex Buoite Stella ,&nbsp;Luigi Murena","doi":"10.1016/j.knee.2025.10.012","DOIUrl":"10.1016/j.knee.2025.10.012","url":null,"abstract":"<div><h3>Background</h3><div>Meniscal root tears (MRTs) are a significant cause of knee pain and dysfunction. An MRT can alter the biomechanics of the knee joint, leading to cartilage damage and osteoarthritis. This systematic review aims to evaluate the impact of MRTs on the progression of knee osteoarthritis in patients undergoing non-operative treatment.</div></div><div><h3>Methods</h3><div>A systematic review was performed based on the PRISMA guidelines in PubMed, Scopus, and ScienceDirect databases (“meniscal root injuries” OR “meniscal root tears”) AND (“osteoarthritis” OR “joint degeneration”).</div></div><div><h3>Results</h3><div>Eight studies, including 1160 patients with MRTs, met the inclusion criteria. MRTs were associated with more severe cartilage damage on the medial femoral condyle (Noyes score, 4.95; <em>P</em> &lt; 0.001) and on the medial tibial plateau (Noyes score, 3.9; <em>P</em> &lt; 0.005) compared with other meniscus tear patterns. Eighty per cent of medial meniscus posterior root tears had an ICRS grade ≥2 chondral lesions at the medial femoral condyle, and those chondral lesions had a more progressive and faster nature compared with medial meniscus posterior horn tears. MRTs and meniscal extrusion were predominant factors associated with accelerated osteoarthritis progression (odds ratio 4.64; 95 % confidence interval, 1.61–13.34; <em>P</em> = 0.004).</div></div><div><h3>Conclusions</h3><div>MRTs have a crucial role both in the initiation and the progression of knee osteoarthritis, leading to severe cartilage damage and dramatic consequences on quality of life. Early diagnosis and appropriate management could preserve knee function and delay osteoarthritic changes.</div></div>","PeriodicalId":56110,"journal":{"name":"Knee","volume":"58 ","pages":"Article 104258"},"PeriodicalIF":2.0,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145477224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}