Combined use of MRI and MRM can be recommended for symptomatic patients when planning surgery. Although pain severity decreased postoperatively in all patients, this decrease was more pronounced in patients with triangular VCS than in those with oval or circular VCS. During preoperative counseling of patients with LSS, the presence of triangular VCS should be considered. This may improve surgical outcome and patient satisfaction.
BACKGROUND:
The article was planned to make the first evaluation in terms of acute subdural hemorrhages, thinking that it can help in appropriate pathologies by tomography interpretation with the artificial intelligence (AI) method, at least in a way to quickly warn the responsible doctor.
METHODS:
A two-level AI-based hybrid method was developed. The proposed model uses the mask-region convolutional neural network (Mask R-CNN) technique, which is a deep learning model, in the hemorrhagic region’s mask generation stage, and a problem-specific, optimized support vector machines (SVM) technique which is a machine learning model in the binary classification stage. Furthermore, the bee colony algorithm was used for the optimization of SVM algorithms’ parameters.
RESULTS:
In the first stage, the mean average precision (mAP) value was obtained as 0.754 when the intercept over union (IOU) value was taken as 0.5 with the Mask R-CNN architecture used. At the same time, when a 5-fold cross-validation was applied, the mAP value was obtained 0.736. With the hyperparameter optimization for both Mask R-CNN and the SVM algorithm, the accuracy of the two-level classification process was obtained as 96.36%. Furthermore, final false-negative rate and false-positive rate values were obtained as 6.20%, and 2.57%, respectively.
CONCLUSION:
With the proposed model, both the detection of hemorrhage and the presentation of the suspicious area to the physician were performed more successfully on two dimensional (2D) images with low cost and high accuracy compared to similar studies and today’s interpretations with telemedicine techniques.
Paraspinal schwannomas are relatively common but hardly classified subject of spinal neurosurgery. We present a rare and unclassified case of paravertebral schwannoma that causes non-discogenic low back pain. A 45year-old Caucasian female came to our clinic with low back pain. Her pain steadily worsened for two years. Her physical exam showed right straight leg raise positivity at 45-degree angle but provocation test negative. Magnetic resonance imaging showed a mass in the paravertebral muscle originating right lumbar 3-4 facet joint. According to planned surgery patient operated. Histopathological diagnosis confirmed as schwannoma. The patient followed for 2 years without pain and neurologic deficiency. Follow up magnetic resonance imaging showed no residue or recurrence of the mass. Schwannomas must be taken into account for differential diagnosis of unexplained non-discogenic low back pain. New classification systems must be offered based on widespread patient groups.
Objectives: Degenerative lumbar spinal stenosis (DLSS) is the leading cause of pain, disability, and loss of independence in older adults. In this study, the relationship between DLSS and paravertebral muscle thickness and density was investigated using computed tomography (CT) and magnetic resonance imaging (MRI) methods. Thus, the importance of muscles has been examined to take precautions in the name of preventive medicine. Methods: This study was planned as a cross-sectional study. The patient group (n = 77) who had surgery for DLSS and the control group (n = 77) were examined. A total of 154 participants (55 females and 22 males in each group) were evaluated retrospectively in terms of cross-sectional area (CSA) and density in the psoas, erector spina and multifidus muscles. In both groups age, gender and body mass index values equalized. Measurements was averaged from the mid-lumbar 3 level from both sides and multi-points. Results: There was no significant difference between muscle thicknesses (p > 0.05). When evaluated in terms of muscle densities, a significant difference was found between the patient and the control group in terms of psoas muscle (p < 0.05). Likewise, there is the same relationship between erector spinae muscle density and multifidus muscle density (p < 0.05). Conclusions: Roughly no difference was found between the patient and control groups in terms of CSA of the psoas, erector spinae and multifidus muscles, but it was observed that the muscle density, especially in the multifidus, decreased significantly in the patients. Our results suggest that paravertebral muscle density assessment is an important criterion in disease prediction and can inform preventive treatment.
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