Novel Magnetic Resonance Imaging Classification of Osteochondritis Dissecans of the Knee

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Zaamin B. Hussain ◽  
Stephen T. Mathew ◽  
Aliya G. Feroe ◽  
Laura A.B. Lins ◽  
Patricia Miller ◽  
...  
2019 ◽  
Vol 12 (4) ◽  
pp. 284-293 ◽  
Author(s):  
Rens Bexkens ◽  
F. Joseph Simeone ◽  
Denise Eygendaal ◽  
Michel PJ van den Bekerom ◽  
Luke S Oh ◽  
...  

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.


Brain ◽  
2011 ◽  
Vol 134 (12) ◽  
pp. 3742-3754 ◽  
Author(s):  
Jeffrey S. Anderson ◽  
Jared A. Nielsen ◽  
Alyson L. Froehlich ◽  
Molly B. DuBray ◽  
T. Jason Druzgal ◽  
...  

Author(s):  
Mamta Juneja ◽  
Sumindar Kaur Saini ◽  
Jatin Gupta ◽  
Poojita Garg ◽  
Niharika Thakur ◽  
...  

2021 ◽  
Vol 11 (3) ◽  
pp. 352
Author(s):  
Isselmou Abd El Kader ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Sani Saminu ◽  
Imran Javaid ◽  
...  

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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