Magnetic resonance imaging of the temporomandibular joint: Interobserver agreement in subjective classification of disk status

2000 ◽  
Vol 90 (1) ◽  
pp. 102-107 ◽  
Author(s):  
B. Nebbe ◽  
S.L. Brooks ◽  
D. Hatcher ◽  
L.G. Hollender ◽  
N.G.N. Prasad ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Temporomandibular Joint ◽  
Interobserver Agreement ◽  
Resonance Imaging

scholarly journals Interobserver reliability of the classification of capitellar osteochondritis dissecans using magnetic resonance imaging

2019 ◽  
Vol 12 (4) ◽  
pp. 284-293 ◽  
Author(s):  
Rens Bexkens ◽  
F. Joseph Simeone ◽  
Denise Eygendaal ◽  
Michel PJ van den Bekerom ◽  
Luke S Oh ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Osteochondritis Dissecans ◽  
Interobserver Agreement ◽  
Interobserver Reliability ◽  
Lateral Wall ◽  
Magnetic Resonance Images ◽  
Resonance Imaging ◽  
Instability Criteria

Aim (1) To determine the interobserver reliability of magnetic resonance classifications and lesion instability criteria for capitellar osteochondritis dissecans lesions and (2) to assess differences in reliability between subgroups. Methods Magnetic resonance images of 20 patients with capitellar osteochondritis dissecans were reviewed by 33 observers, 18 orthopaedic surgeons and 15 musculoskeletal radiologists. Observers were asked to classify the osteochondritis dissecans according to classifications developed by Hepple, Dipaola/Nelson, Itsubo, as well as to apply the lesion instability criteria of DeSmet/Kijowski and Satake. Interobserver agreement was calculated using the multirater kappa (k) coefficient. Results Interobserver agreement ranged from slight to fair: Hepple (k = 0.23); Dipaola/Nelson (k = 0.19); Itsubo (k = 0.18); DeSmet/Kijowksi (k = 0.16); Satake (k = 0.12). When classifications/instability criteria were dichotomized into either a stable or unstable osteochondritis dissecans, there was more agreement for Hepple (k = 0.52; p = .002), Dipaola/Nelson (k = 0.38; p = .015), DeSmet/Kijowski (k = 0.42; p = .001) and Satake (k = 0.41; p < .001). Overall, agreement was not associated with the number of years in practice or the number of osteochondritis dissecans cases encountered per year (p > .05). Conclusion One should be cautious when assigning grades using magnetic resonance classifications for capitellar osteochondritis dissecans. When making treatment decisions, one should rather use relatively simple distinctions (e.g. stable versus unstable osteochondritis dissecans; lateral wall intact versus not intact), as these are more reliable.

scholarly journals Diagnostic performance of magnetic resonance imaging in the assessment of periosteal reactions in bone sarcomas using conventional radiography as the reference

2017 ◽  
Vol 50 (3) ◽  
pp. 176-181 ◽  
Author(s):  
José Luiz de Sá Neto ◽  
Marcelo Novelino Simão ◽  
Michel Daoud Crema ◽  
Edgard Eduard Engel ◽  
Marcello Henrique Nogueira-Barbosa
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Diagnostic Performance ◽  
Interobserver Agreement ◽  
High Specificity ◽  
Conventional Radiography ◽  
Periosteal Reaction ◽  
Resonance Imaging ◽  
Significant Difference

Abstract Objective: To evaluate the performance of magnetic resonance imaging (MRI) in detecting periosteal reactions and to compare MRI and conventional radiography (CR) in terms of the classification of periosteal reactions. Materials and Methods: Retrospective study of 42 consecutive patients (mean age, 22 years; 20 men) with a confirmed diagnosis of osteosarcoma or Ewing's sarcoma, MRI and CR images having been acquired pretreatment. Three blinded radiologists detected periosteal reactions and evaluated each periosteal reaction subtype in CR and MRI images: Codman's triangle; laminated; and spiculated. The CR was used as a benchmark to calculate the diagnostic performance. We used the kappa coefficient to assess interobserver reproducibility. A two-tailed Fisher's exact test was used in order to assess contingency between CR and MRI classifications. Results: In the detection of periosteal reactions, MRI showed high specificity, a high negative predictive value, and low-to-moderate sensitivity. For CR and for MRI, the interobserver agreement for periosteal reaction was almost perfect, whereas, for the classification of different subtypes of periosteal reaction, it was higher for the Codman's triangle subtype and lower for the spiculated subtype. There was no significant difference between MRI and CR in terms of the classifications (p < 0.05). Conclusion: We found no difference between MRI and CR in terms of their ability to classify periosteal reactions. MRI showed high specificity and almost perfect interobserver agreement for the detection of periosteal reactions. The interobserver agreement was variable for the different subtypes of periosteal reaction.

Interobserver Agreement in the Classification of Rotator Cuff Tears Using Magnetic Resonance Imaging

2008 ◽  
Vol 36 (1) ◽  
pp. 99-103 ◽  
Author(s):  
Edwin E. Spencer ◽  
Warren R. Dunn ◽  
Rick W. Wright ◽  
Brian R. Wolf ◽  
Kurt P. Spindler ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Rotator Cuff ◽  
Interobserver Agreement ◽  
Rotator Cuff Tears ◽  
Resonance Imaging

Survey of denoising, segmentation and classification of magnetic resonance imaging for prostate cancer

2021 ◽  
Author(s):  
Mamta Juneja ◽  
Sumindar Kaur Saini ◽  
Jatin Gupta ◽  
Poojita Garg ◽  
Niharika Thakur ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Prostate Cancer ◽  
Magnetic Resonance ◽  
Resonance Imaging

scholarly journals Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

2021 ◽  
Vol 11 (3) ◽  
pp. 352
Author(s):  
Isselmou Abd El Kader ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Sani Saminu ◽  
Imran Javaid ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Medical Image ◽  
Resonance Imaging ◽  
Feature Maps ◽  
Deep Cnn

The classification of brain tumors is a difficult task in the field of medical image analysis. Improving algorithms and machine learning technology helps radiologists to easily diagnose the tumor without surgical intervention. In recent years, deep learning techniques have made excellent progress in the field of medical image processing and analysis. However, there are many difficulties in classifying brain tumors using magnetic resonance imaging; first, the difficulty of brain structure and the intertwining of tissues in it; and secondly, the difficulty of classifying brain tumors due to the high density nature of the brain. We propose a differential deep convolutional neural network model (differential deep-CNN) to classify different types of brain tumor, including abnormal and normal magnetic resonance (MR) images. Using differential operators in the differential deep-CNN architecture, we derived the additional differential feature maps in the original CNN feature maps. The derivation process led to an improvement in the performance of the proposed approach in accordance with the results of the evaluation parameters used. The advantage of the differential deep-CNN model is an analysis of a pixel directional pattern of images using contrast calculations and its high ability to classify a large database of images with high accuracy and without technical problems. Therefore, the proposed approach gives an excellent overall performance. To test and train the performance of this model, we used a dataset consisting of 25,000 brain magnetic resonance imaging (MRI) images, which includes abnormal and normal images. The experimental results showed that the proposed model achieved an accuracy of 99.25%. This study demonstrates that the proposed differential deep-CNN model can be used to facilitate the automatic classification of brain tumors.

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