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Outcome Of Surgical Treatment Of Tuberculosis Of The Spine In Patients With Motor Deficits
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Background: Significant numbers of patients with spinal tuberculosis (TB), especially in developing countries, still present late after disease onset with severe neurological deficits.

Objective:This study was conducted to assess the outcome of surgery in patients with tuberculosis of the spine with motor deficits.

Type of the study: Retrospective study.

Methods: We retrospectively analyzed data obtained in all the patients with severe motor deficits due to spinal TB admitted to and surgically treated in four hospitals in Baghdad/Iraq during the period from January 2012 to January 2014. History, examination, imaging, histological, postoperative, and follow-up data were retrospectively culled from hospitals records and then analyzed.  Data obtained in 48 patients with 6-24 months of follow up (mean follow-up period 12.8 months) were analyzed. The disease in 34 patients was characterized by Frankel Grade A/B and in 14 patients by Frankel Grade C at admission.

Results: Thirty (88%) of the 34 patients with Frankel Grade A/B status and 13 (92.8%) of the 14 patients with Frankel Grade C status at admission experienced improvement to Frankel Grade D/E (walking with or without support) at the last follow-up examination after surgery. The degree of improvement exhibited by patients with a Frankel Grade A/B spinal cord injury was comparable to that shown by patients with Frankel Grade C status. Even patients with flaccid paraplegia, gross sensory deficit, prolonged weakness, spinal cord signal changes demonstrated on magnetic resonance imaging, and bladder involvement have experienced dramatic improvement in motor function since surgery. A significant number of the patients have shown remarkable improvement in other symptoms such as pain (91.6%), spasticity (88%), and bladder symptoms (88%).

Conclusions: A significant proportion of patients with spinal TB and severe motor deficits experience remarkable improvement after surgical decompression and hence should undergo surgery even though they may be suffering from paraplegia of considerable duration

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Publication Date
Sat Jan 20 2024
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Enhanced Support Vector Machine Methods Using Stochastic Gradient Descent and Its Application to Heart Disease Dataset
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Support Vector Machines (SVMs) are supervised learning models used to examine data sets in order to classify or predict dependent variables. SVM is typically used for classification by determining the best hyperplane between two classes. However, working with huge datasets can lead to a number of problems, including time-consuming and inefficient solutions. This research updates the SVM by employing a stochastic gradient descent method. The new approach, the extended stochastic gradient descent SVM (ESGD-SVM), was tested on two simulation datasets. The proposed method was compared with other classification approaches such as logistic regression, naive model, K Nearest Neighbors and Random Forest. The results show that the ESGD-SVM has a

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Publication Date
Thu Jun 30 2022
Journal Name
Iraqi Journal Of Science
Brain MR Images Classification for Alzheimer’s Disease
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    Alzheimer’s Disease (AD) is the most prevailing type of dementia. The prevalence of AD is estimated to be around 5% after 65 years old and is staggering 30% for more than 85 years old in developed countries. AD destroys brain cells causing people to lose their memory, mental functions and ability to continue daily activities. The findings of this study are likely to aid specialists in their decision-making process by using patients’ Magnetic Resonance Imaging (MRI) to distinguish patients with AD from Normal Control (NC). Performance evolution was applied to 346 Magnetic Resonance images from the Alzheimer's Neuroimaging Initiative (ADNI) collection. The Deep Belief Network (DBN) classifier was used to fulfill classification f

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