scholarly journals The Use of Artificial Intelligence in the Evaluation of Knee Pathology

2020 ◽  
Vol 24 (01) ◽  
pp. 021-029 ◽  
Author(s):  
Elisabeth R. Garwood ◽  
Ryan Tai ◽  
Ganesh Joshi ◽  
George J. Watts V
Keyword(s):  
Magnetic Resonance Imaging ◽  
Artificial Intelligence ◽  
Magnetic Resonance ◽  
Knee Osteoarthritis ◽  
Bone Tumors ◽  
Quantitative Assessment ◽  
Tendon Healing ◽  
Meniscal Tears ◽  
Resonance Imaging ◽  
Knee Pathology

AbstractArtificial intelligence (AI) holds the potential to revolutionize the field of radiology by increasing the efficiency and accuracy of both interpretive and noninterpretive tasks. We have only just begun to explore AI applications in the diagnostic evaluation of knee pathology. Experimental algorithms have already been developed that can assess the severity of knee osteoarthritis from radiographs, detect and classify cartilage lesions, meniscal tears, and ligament tears on magnetic resonance imaging, provide automatic quantitative assessment of tendon healing, detect fractures on radiographs, and predict those at highest risk for recurrent bone tumors. This article reviews and summarizes the most current literature.

scholarly journals Magnetic resonance imaging in the evaluation of meniscal tear

2010 ◽  
Vol 4 (2) ◽  
pp. 215-222
Author(s):  
Numphung Numkarunarunrote ◽  
Anoma Sanpatchayapong ◽  
Pongsak Yuktanandana ◽  
Somsak Kuptniratsaikul
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Predictive Value ◽  
Meniscal Tear ◽  
Posterior Horn ◽  
Anterior Horn ◽  
Meniscal Tears ◽  
Resonance Imaging ◽  
Sensitivity Specificity ◽  
Knee Pathology

Abstract Background: Magnetic resonance imaging (MRI) has been recognized as the imaging method for non-invasive evaluation of knee pathology, particular meniscus and ligaments. Objective: Compare the sensitivity, specificity, and accuracy of MRI in the detection of meniscal tears with arthroscopy. Material and methods: Twenty-seven patients who were diagnosed as meniscal tear on arthroscopy with preoperative MRI were included in this study between January 2003 and June 2008. MRI was performed with a 1.5 Tesla Signa Horizon Echospeed MRI for eight patients between January 2003 and June 2005 and a 1.5 Tesla Signa Excited HD MRI for nineteen patients between July 2005 and June 2008. The location of meniscal tear was evaluated by studying three areas: anterior horn, body and posterior horn. Sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of the anterior horn, body, posterior horn and overall meniscus were calculated. Results: The sensitivity of MRI for detecting meniscal tears at the anterior horn, body, posterior horn, and overall medial meniscus was 42.9%, 87.5%, 94.1%, and 81.3%, respectively. The specificity was 95.0%, 84.2%, 81.8%, and 88.0%, respectively. The accuracy was 81.5%, 85.2%, 89.3%, and 85.4%, respectively. The PPV was 75.0%, 70.0%, 88.9%, and 81.2%, respectively. The NPV was 82.6%, 94.1%, 90.0%, and 88.0%, respectively. The sensitivity of MRI for detecting meniscal tears at the anterior horn, body, posterior horn and overall lateral meniscus was 0%, 100%, 85.7%, and 80.0%, respectively. The specificity was 100%, 100%, 90.5% and 97.2%, respectively. The accuracy was 96.0%, 100%, 90.5%, and 97.2%, respectively. The PPV was 100%, 75% and 80%, respectively. The NPV was 96.3%, 100%, 95.0%, and 97.2%, respectively. Conclusion: MRI is a helpful technique to detect meniscal tear with different sensitivity and accuracy on the meniscal location.

scholarly journals Relationship between abnormalities detected by magnetic resonance imaging and knee symptoms in early knee osteoarthritis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Seiya Ota ◽  
Eiji Sasaki ◽  
Shizuka Sasaki ◽  
Daisuke Chiba ◽  
Yuka Kimura ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Knee Osteoarthritis ◽  
Cartilage Damage ◽  
Resonance Imaging ◽  
Mri Findings ◽  
Radiographic Evidence ◽  
Cross Sectional ◽  
Meniscal Lesions ◽  
Magnetic Resonance Imaging Mri

AbstractWe investigated the prevalence of magnetic resonance imaging (MRI) findings and their relationship with knee symptoms in women without radiographic evidence of knee osteoarthritis (KOA). This cross-sectional cohort study included 359 Japanese women without radiographic evidence of KOA (Kellgren‒Lawrence grade < 2). All participants underwent T2-weighted fat-suppressed MRI of their knees. Structural abnormalities (cartilage damage, bone marrow lesions [BMLs], subchondral cysts, bone attrition, osteophytes, meniscal lesions, and synovitis) were scored according to the whole-organ MRI score method. Knee symptoms were evaluated using the Knee Injury and Osteoarthritis Outcome Score. Participants were divided into early and non-KOA groups based on early KOA classification criteria. Logistic regression analysis was performed to evaluate the relationship between MRI abnormalities and knee symptoms. Cartilage damage was the most common abnormality (43.5%). The prevalences of cartilage damage, BMLs, subchondral cysts, bone attrition, meniscal lesions, and synovitis were higher in patients with early KOA than in those without. Synovitis (odds ratio [OR] 2.254, P = 0.002) and meniscal lesions (OR 1.479, P = 0.031) were positively associated with the presence of early KOA. Synovitis was most strongly associated with knee pain and might be a therapeutic target in patients with early KOA.

Evaluation of Recurring Meniscal Tears with Gadolinium-Enhanced Magnetic Resonance Imaging

2003 ◽  
Vol 31 (6) ◽  
pp. 868-873 ◽  
Author(s):  
Michael J. Vives ◽  
David Homesley ◽  
Michael G. Ciccotti ◽  
Mark E. Schweitzer
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Meniscal Tears ◽  
Resonance Imaging
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