The Value of the Rehabilitation for Cases with Facial Palsy after Removal of Brain Tumor Schwannoma (Case Study)

2019 ◽  
Vol 9 (1) ◽  
pp. 81-87
Keyword(s):  
Author(s):  
Deepthi Murthy T. S. ◽  
Sadashivappa G.

Usage of grayscale format of radiological images is proportionately more as compared to that of colored one. This format of medical image suffers from all the possibility of improper clinical inference which will lead to error-prone analysis in further usage of such images in disease detection or classification. Therefore, we present a framework that offers single-window operation with a set of image enhancing algorithm meant for further optimizing the visuality of medical images. The framework performs preliminary pre-processing operation followed by implication of linear and non-linear filter and multi-level image enhancement processes. The significant contribution of this study is that it offers a comprehensive mechanism to implement the various enhancement schemes in highly discrete way that offers potential flexibility to physical in order to draw clinical conclusion about the disease being monitored. The proposed system takes the case study of brain tumor to implement to testify the framework.


2021 ◽  
Vol 38 (1) ◽  
pp. 66-71
Author(s):  
Chae Hyun Park ◽  
Jae Hui Kang ◽  
Hwa Yeon Ryu ◽  
Ga Hyeon Jung ◽  
Yong Ho Ku ◽  
...  

Miller Fisher syndrome (MFS) is a rare variant of Guillain?Barr? syndrome characterized by ocular paralysis, ataxia, and insensitivity. This report describes the effect of Complex Korean Medicine Treatment (CKMT) on a patient previously diagnosed with MFS presenting with diplopia and facial palsy. The distance at which diplopia occurs, the diplopia questionnaire, the range of diplopia, the degree of facial paralysis, and the degree of ptosis were evaluated at the time of admission and weekly for 1 month. After receiving CKMT for 4 weeks the 62-year-old female had improved symptoms of diplopia, bilateral facial palsy and ptosis caused by MFS. These results show the significant association of MFS with facial paralysis and the improvement achieved with CKMT.


Author(s):  
Eun Ji Lee ◽  
Sung Tae Kim ◽  
Min Gu Kwon ◽  
Hyun Kwon Shin ◽  
Yong Jun Koh ◽  
...  
Keyword(s):  

2017 ◽  
Vol 37 (7) ◽  
pp. 731-734
Author(s):  
Munenori KUSUNOKI ◽  
Takeshi UMEGAKI ◽  
Miki TAMAI ◽  
Yuki NIKAIDO ◽  
Makiko MIKAMI ◽  
...  

2010 ◽  
Vol 125 (4) ◽  
pp. 405-409 ◽  
Author(s):  
R Hirai ◽  
M Ikeda ◽  
H Kishi ◽  
Y Nomura ◽  
S Shigihara

AbstractObjective:Only a few benign tumours of the middle ear have been reported to lead to the development of facial palsy. Here, we describe a patient with middle-ear cavernous lymphangioma and facial palsy.Study design:Single case study.Patient:A 61-year-old man presented with left-sided hearing impairment and incomplete left facial palsy. A tumour was confirmed to be occupying the epi- to mesotympanum and to be joined to the facial nerve. The tumour was removed along with facial nerve tissue, which was resected at its horizontal portion, and the remaining facial nerve was fixed by end-to-end anastomosis. Complete facial paralysis occurred after the operation, but the patient's House–Brackmann grade gradually improved to grade III. Post-operative histopathological examination revealed infiltration of the lymphangioma into the facial nerve tissue, together with mild neural atrophy of the facial nerve.Conclusion:These findings suggested that tumour invasion was the cause of facial palsy in this patient.


The brain tumor detection continues to be a challenge owing to the complexity of its symptoms. The research era indicates the tumor diagnosis and identification of tumor exact indicators are still uncertain. These tumors can appear anywhere in the brain and have any kind of shape, size, and contrast. The brain tumor exploration with deep learning is a solution for flexible, high capacity and extreme efficiency. The deep learning is an application of the artificial intelligence with multiple layers helping to predict the outcome of the disease early detection. This paper presents an approach to recognize the indicators and show that deep learning drops error rate for brain tumor diagnoses by 80%.


2018 ◽  
Vol 8 (4) ◽  
pp. 325-331
Author(s):  
Iwona Twardak ◽  
Aleksandra Lisowska ◽  
Dominika Pogłódek ◽  
Jerzy Twardak ◽  
Dominik Krzyżanowski

2017 ◽  
Vol 1 (1) ◽  
pp. 15
Author(s):  
Afif Rofiky ◽  
Paulus Rahardjo ◽  
Didik Soeharmanto

Background : Diffusion Tensor Imaging (DTI), namely MRI sequence which is the diffusion of water analysis that shows the complex structure of brain tissue. The weakness of this sequence is scanning time. Number of Diffusion Gradient Direction (NDGD) is one of parameter that effect scanning time. Purpose: This study has aim to compare between NDGD 25 and NDGD 15 in brain tumor. Methods: This study used observational analytic study with prospective approach. Five patients were examined using DTI sequence with NDGD 25 and NDGD 15. The parameter for evaluating the quality image is of Fractional Anisotropy (FA) and Fiber Tracking (FT). Result: Image with NDGD 25 was better than NDGD 15, but the difference was not significantly. Conclusion: It can be concluded that NDGD 15 can be solution to get informative image with short scan time when DTI sequence is used to examine brain tumor.


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