Multirater agreement for grading the femoral and tibial cartilage surface lesions at CT arthrography and analysis of causes of disagreement

Omoumi, Patrick; Michoux, Nicolas; Larbi, Ahmed; Lacoste, L; Lecouvet, Frédéric; Perlepe, Vasiliki; Berg, Bruno C. Vande

doi:10.1016/j.ejrad.2016.12.026

Cited by 15 publications

(9 citation statements)

References 32 publications

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“…8,9,35 The semi-quantitative scoring of cartilage lesions based on CT arthrography has limitation with a low inter-observer agreement. 20 The presented method gives additional information compared to the KL classification on radiographs, which is based on a qualitative evaluation of the JSW variation and also on the presence of osteophytes. With a normal JS and a doubtful or definite osteophyte, the KL classification is KL ¼ 1 and KL ¼ 2, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…13 Imaging by CT is not used in routine clinical investigations of knee disease because a significant radiation dose is delivered; however, it is known to provide excellent visualization of bone and calcified tissue, and its use has been proposed to evaluate calcium deposition, 18 quantitative analysis of regional bone mineral density, 19 and semi-quantitative scoring of cartilage lesions with arthrography. 20 High resolution peripheral QCT (HR-pQCT) images are clinically used to study separately trabecular bone density and micro-architecture and cortical bone density, thickness, and porosity. 21 Recently, new developments have been performed to study bone micro-architecture of the human knee in vivo.…”

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confidence: 99%

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Three‐dimensional mapping of the joint space for the diagnosis of knee osteoarthritis based on high resolution computed tomography: Comparison with radiographic, outerbridge, and meniscal classifications

Mezlini-Gharsallah

Youssef

et al. 2018

Journal Orthopaedic Research

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One of the most important characteristic of knee osteoarthritis (OA) is the joint space (JS) width narrowing. Measurements are usually performed on two dimensional (2D) X‐rays. We propose and validate a new method to assess the 3D joint space at the medial knee compartment using high resolution peripheral computed tomography images. A semi‐automated method was developed to obtain a distance 3D map between femur an tibia with the following parameters: volume, minimum, maximum, mean, standard deviation, median, asymmetry, and entropy. We analyzed 71 knee specimens (mean age: 85 years), radiographs were performed for the Kellgren Lawrence (KL) score grading. In a subgroup of 41 specimens, the histopathological Outerbridge and meniscal classifications were performed and then cores were harvested from the tibial plateau in three different positions (posterior, central, and peripheral) and imaged at 10 µm of resolution to measure the cartilage thickness. Minimum, maximum, mean, and median were statistically lower and entropy higher between knee specimens classified as KL = 0 and KL = 3–4. Gr1 and 2 were statistically different from Gr3‐4 for minimum, asymmetry, entropy using the Outerbridge classification and Gr1 was statistically different from Gr3–4 using the meniscal classification. Asymmetry, minimum, mean, median and entropy were significantly correlated with cartilage thickness. Parameters extracted from a 3D map of the medial joint space indicate local variations of JS and are related to local measurements of tibial cartilage thickness, and could be consequently useful to identify early OA. © 2018 The Authors. Journal of Orthopaedic Research® Published by Wiley Periodicals, Inc. on behalf of Orthopaedic Research Society. J Orthop Res 36:2380–2391, 2018.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

Three‐dimensional mapping of the joint space for the diagnosis of knee osteoarthritis based on high resolution computed tomography: Comparison with radiographic, outerbridge, and meniscal classifications

Mezlini-Gharsallah

Youssef

et al. 2018

Journal Orthopaedic Research

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“…Considering the low interobserver agreement and moderate diagnostic performance of clinical radiologists to perform some of these tasks, it would be interesting to compare AI techniques with a validated gold standard such as arthroscopic results, whenever available. 42,43 Semiquantitative Analysis of the Spine…”

Section: Automatic Classification Of Images Semiquantitative Analysismentioning

confidence: 99%

Artificial Intelligence in Musculoskeletal Imaging: Review of Current Literature, Challenges, and Trends

Hirschmann

Cyriac

Stieltjes

et al. 2019

Semin Musculoskelet Radiol

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Artificial intelligence (AI) has gained major attention with a rapid increase in the number of published articles, mostly recently. This review provides a general understanding of how AI can or will be useful to the musculoskeletal radiologist. After a brief technical background on AI, machine learning, and deep learning, we illustrate, through examples from the musculoskeletal literature, potential AI applications in the various steps of the radiologist's workflow, from managing the request to communication of results. The implementation of AI solutions does not go without challenges and limitations. These are also discussed, as well as the trends and perspectives.

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“…48 When it is not clinically feasible to obtain an MRI due to the patient being claustrophobic, or when patients have non-MRI compatible implanted medical devices, computed tomography (CT) arthrography may be used to evaluate for cartilaginous injuries. [49][50][51] US has a limited role in the evaluation of focal cartilaginous defects given its inability to evaluate the entire cartilaginous surface; however, it is being utilized more often within the clinic to evaluate and monitor osteoarthritis. 52,53 US can also play a role in the evaluation of crystal-induced arthropathies.…”

Section: Cartilaginous Pathologymentioning

confidence: 99%

Impact of Clinical Presentation on Imaging Evaluation to Direct Effective Treatment Strategies

et al. 2017

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A multitude of musculoskeletal disorders of the knee are commonly encountered in clinical practice. Ensuring an accurate diagnosis can be challenging. It is critical to establish a thoughtful and systematic approach to assessing the patient using history and physical exam followed by appropriate imaging studies. The physical exam may be complicated due to various test maneuvers designed to help make the diagnosis. It is also important to understand the limitations of each physical exam maneuver. When appropriate, imaging is obtained to help make the diagnosis and guide treatment. There are various imaging examinations to choose from when evaluating the knee, including X-rays, computed tomography, magnetic resonance imaging, and ultrasound. An algorithmic approach to choosing the best imaging study is often helpful to avoid unnecessary costs and burden to the patients. The combination of a thorough clinical assessment and appropriate imaging examinations will result in accurate diagnosis, which directs proper treatment.

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Multirater agreement for grading the femoral and tibial cartilage surface lesions at CT arthrography and analysis of causes of disagreement

Cited by 15 publications

References 32 publications

Three‐dimensional mapping of the joint space for the diagnosis of knee osteoarthritis based on high resolution computed tomography: Comparison with radiographic, outerbridge, and meniscal classifications

Three‐dimensional mapping of the joint space for the diagnosis of knee osteoarthritis based on high resolution computed tomography: Comparison with radiographic, outerbridge, and meniscal classifications

Artificial Intelligence in Musculoskeletal Imaging: Review of Current Literature, Challenges, and Trends

Impact of Clinical Presentation on Imaging Evaluation to Direct Effective Treatment Strategies

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