Pain neuroscience education following arthroscopic rotator cuff repair for patients with rotator cuff tears: a double blind randomized controlled clinical trial.

OBJECTIVE: This trial examines the efficacy of the Pain Neuroscience Education (PNE) on clinical outcomes in patients with arthroscopic rotator cuff repair (ARCR). DESIGN: A total of 36 participants undergoing ARCR were assigned to either the experimental group (n = 18) or control group (n = 18) in this randomized study. A 6-week-long conventional physiotherapy program was administered for both groups. In addition, a PNE protocol was administered for the experimental group for a whole period of 6 weeks (one session/week, 15-60 min per session). The primary outcomes were to compare pre- and post-treatment scores of the experimental versus control groups on the pain and disability. Our secondary outcomes included the comparisons of scores on the catastrophizing, anxiety, depression, kinesiophobia, and quality of life. The participants were assessed both at baseline and post-treatment. RESULTS: The improvement in pain catastrophizing, anxiety, depression and kinesiophobia was greater in the experimental group (p < 0.05). The improvement was similar in both groups in terms of the rest of outcome measures. CONCLUSION: This study showed that the PNE improved only psychological aspects of the chronic pain in ARCR. Therefore, adding PNE to the conventional program might be useful to improve pain catastrophizing, anxiety, depression and kinesiophobia in patients with ARCR.

The effects of dynamic neuromuscular stabilization approach on clinical outcomes in older patients with chronic nonspecific low back pain: a randomized, controlled clinical trial.

OBJECTIVE: We aimed to examine the effects of Dynamic Neuromuscular Stabilization (DNS) approach in older patients with chronic non-specific low back pain (CNSLBP). METHODS: A total of 72 participants with CNSLBP were assigned to either the experimental group (n = 36) or control group (n = 36) in this randomized study. A conventional physiotherapy program was administered to the participants in the control group for 3 days per week for a total of 6 weeks. In addition to the conventional program, DNS exercise protocol was performed for 3 days per week for 6 weeks for the participants in the experimental group. While quality of movements and exercise capacity were our primary outcomes, functional balance and quality of life constituted our secondary outcomes. The participants were assessed both at baseline and post-treatment. RESULTS: The improvement in a deep squat, in-line lunge, hurdle step, shoulder flexibility, rotary trunk stability, total Functional Movement Screening score, and Timed-up and Go Test score was greater in the experimental group (p<.05). The improvement was similar in both groups in terms of the rest of outcome measures. DISCUSSION: This study demonstrated the effectiveness of the DNS approach on some functional movement patterns and functional balance performance in older patients with CNSLBP.

Biopsychosocial Approach in Identifying Risk Factors of Kinesiophobia in Persons with Subacromial Pain Syndrome and Developing a Clinical Prediction Tool.

Introduction: Although the negative effects of kinesiophobia on functional status in subacromial pain syndrome (SAPS) patients are clearly demonstrated, no study examines the risk factors of kinesiophobia in individuals with SAPS from a biopsychosocial perspective. The present study aims to determine the risk factors of kinesiophobia in individuals with SAPS using a biopsychosocial approach. This study also aims to explore the compounding effects of multiple associative risk factors by developing a clinical prediction tool to identify SAPS patients at higher risk for kinesiophobia. Materials and methods: This cross-sectional study included 549 patients who were diagnosed with SAPS. The Tampa-Scale of Kinesiophobia (TSK) was used to assess kinesiophobia. Visual analog scale (VAS), The Shoulder Pain and Disability Index (SPADI), Disabilities of the Arm, Shoulder, and Hand (DASH) questionnaire, the presence of metabolic syndrome, using any non-steroidal anti-inflammatory drugs, Pain Catastrophizing Scale (PCS), Illness Perception Questionnaire-revised (IPQ-R), Hospital Anxiety and Depression Scale (HADS), behavioral pattern of the patient, sociodemographic characteristics, and treatment expectancy were outcome measures. Results: Thirteen significant risk factors of having kinesiophobia were: VASat rest (≥ 5.2), VASduring activity (≥ 7.1), DASH (≥ 72.1), presence of metabolic syndrome, PCShelplessness (≥ 16.1), IPQ-Rpersonal control (≤ 17.1), IPQ-Rtreatment control (≤ 16.3), HADSdepression (≥ 7.9), avoidance behavior type, being female, educational level (≤ high school), average hours of sleep (≤ 6.8), and treatment expectancy (≤ 6.6). The presence of seven or more risk factors increased the probability of having high level of kinesiophobia from 34.3 to 51%. Conclusions: It seems necessary to address these factors, increase awareness of health practitioners and individuals. Level of evidence: Level IV.

An automated COVID-19 detection based on fused dynamic exemplar pyramid feature extraction and hybrid feature selection using deep learning.

The new coronavirus disease known as COVID-19 is currently a pandemic that is spread out the whole world. Several methods have been presented to detect COVID-19 disease. Computer vision methods have been widely utilized to detect COVID-19 by using chest X-ray and computed tomography (CT) images. This work introduces a model for the automatic detection of COVID-19 using CT images. A novel handcrafted feature generation technique and a hybrid feature selector are used together to achieve better performance. The primary goal of the proposed framework is to achieve a higher classification accuracy than convolutional neural networks (CNN) using handcrafted features of the CT images. In the proposed framework, there are four fundamental phases, which are preprocessing, fused dynamic sized exemplars based pyramid feature generation, ReliefF, and iterative neighborhood component analysis based feature selection and deep neural network classifier. In the preprocessing phase, CT images are converted into 2D matrices and resized to 256 × 256 sized images. The proposed feature generation network uses dynamic-sized exemplars and pyramid structures together. Two basic feature generation functions are used to extract statistical and textural features. The selected most informative features are forwarded to artificial neural networks (ANN) and deep neural network (DNN) for classification. ANN and DNN models achieved 94.10% and 95.84% classification accuracies respectively. The proposed fused feature generator and iterative hybrid feature selector achieved the best success rate, according to the results obtained by using CT images.

A novel Covid-19 and pneumonia classification method based on F-transform.

Nowadays, Covid-19 is the most important disease that affects daily life globally. Therefore, many methods are offered to fight against Covid-19. In this paper, a novel fuzzy tree classification approach was introduced for Covid-19 detection. Since Covid-19 disease is similar to pneumonia, three classes of data sets such as Covid-19, pneumonia, and normal chest x-ray images were employed in this study. A novel machine learning model, which is called the exemplar model, is presented by using this dataset. Firstly, fuzzy tree transformation is applied to each used chest image, and 15 images (3-level F-tree is constructed in this work) are obtained from a chest image. Then exemplar division is applied to these images. A multi-kernel local binary pattern (MKLBP) is applied to each exemplar and image to generate features. Most valuable features are selected using the iterative neighborhood component (INCA) feature selector. INCA selects the most distinctive 616 features, and these features are forwarded to 16 conventional classifiers in five groups. These groups are decision tree (DT), linear discriminant (LD), support vector machine (SVM), ensemble, and k-nearest neighbor (k-NN). The best-resulted classifier is Cubic SVM, and it achieved 97.01% classification accuracy for this dataset.

An automated Residual Exemplar Local Binary Pattern and iterative ReliefF based COVID-19 detection method using chest X-ray image.

Coronavirus is normally transmitted from animal to person, but nowadays it is transmitted from person to person by changing its form. Covid-19 appeared as a very dangerous virus and unfortunately caused a worldwide pandemic disease. Radiology doctors use X-ray or CT images for the diagnosis of Covid-19. It has become crucial to help diagnose such images using image processing methods. Therefore, a novel intelligent computer vision method to automatically detect the Covid-19 virus was proposed. The proposed automatic Covid-19 detection method consists of preprocessing, feature extraction, and feature selection stages. Image resizing and grayscale conversion are used in the preprocessing phase. The proposed feature generation method is called Residual Exemplar Local Binary Pattern (ResExLBP). In the feature selection phase, a novel iterative ReliefF (IRF) based feature selection is used. Decision tree (DT), linear discriminant (LD), support vector machine (SVM), k nearest neighborhood (kNN), and subspace discriminant (SD) methods are chosen as classifiers in the classification phase. Leave one out cross-validation (LOOCV), 10-fold cross-validation, and holdout validation are used for training and testing. In this work, SVM classifier achieved 100.0% classification accuracy by using 10-fold cross-validation. This result clearly has shown that the perfect classification rate by using X-ray image for Covid-19 detection. The proposed ResExLBP and IRF based method is also cognitive, lightweight, and highly accurate.

An expert system for brain tumor detection: Fuzzy C-means with super resolution and convolutional neural network with extreme learning machine.

Super-resolution, which is one of the trend issues of recent times, increases the resolution of the images to higher levels. Increasing the resolution of a vital image in terms of the information it contains such as brain magnetic resonance image (MRI), makes the important information in the MRI image more visible and clearer. Thus, it is provided that the borders of the tumors in the related image are found more successfully. In this study, brain tumor detection based on fuzzy C-means with super-resolution and convolutional neural networks with extreme learning machine algorithms (SR-FCM-CNN) approach has been proposed. The aim of this study has been segmented the tumors in high performance by using Super Resolution Fuzzy-C-Means (SR-FCM) approach for tumor detection from brain MR images. Afterward, feature extraction and pretrained SqueezeNet architecture from convolutional neural network (CNN) architectures and classification process with extreme learning machine (ELM) were performed. In the experimental studies, it has been determined that brain tumors have been better segmented and removed using SR-FCM method. Using the SquezeeNet architecture, features were extracted from a smaller neural network model with fewer parameters. In the proposed method, 98.33% accuracy rate has been detected in the diagnosis of segmented brain tumors using SR-FCM. This rate is greater 10% than the rate of recognition of brain tumors segmented with fuzzy C-means (FCM) without SR.

White blood cells detection and classification based on regional convolutional neural networks.

White blood cells (WBC) are important parts of our immune system and they protect our body against infections by eliminating viruses, bacteria, parasites and fungi. There are five types of WBC. These are called Lymphocytes, Monocytes, Eosinophils, Basophils and Neutrophils. The number of WBC types and the total number of WBCs provide important information about our health status. Diseases such as leukemia, AIDS, autoimmune diseases, immune deficiencies, blood diseases can be diagnosed based on the number of WBCs. In this study, a computer-aided automated system that can easily identify and locate WBC types in blood images has been proposed. Current blood test devices usually detect WBCs with traditional image processing methods such as preprocessing, segmentation, feature extraction, feature selection and classification. Deep learning methodology is superior to traditional image processing methods in literature. In addition, traditional methods require the appearance of the whole object to be able to recognize objects. Contrary to traditional methods, convolutional neural networks (CNN), a deep learning architecture, can extract features from a part of an object and perform object recognition. In this case, a CNN-based system shows a higher performance in recognizing partially visible cells for reasons such as overlap or only partial visibility of the image. Therefore, it has been the motivation of this study to increase the performance of existing blood test devices with deep learning method. Blood cells have been identified and classified by Regional Based Convolutional Neural Networks. Designed architectures have been trained and tested by combining BCCD data set and LISC data set. Regional Convolutional Neural Networks (R - CNN) has been used as a methodology. In this way, different cell types within the same image have been classified simultaneously with a detector. While training CNN which is the basis of R - CNN architecture; AlexNet, VGG16, GoogLeNet, ResNet50 architectures have been tested with full learning and transfer learning. At the end of the study, the system has showed 100% success in determining WBC cells. ResNet50, one of the CNN architectures, has showed the best performance with transfer learning. Cell types of Lymphocyte were determined with 99.52% accuracy rate, Monocyte with 98.40% accuracy rate, Basophil with 98.48% accuracy rate, Eosinophil with 96.16% accuracy rate and Neutrophil with 95.04% accuracy rate.

A new approach for brain tumor diagnosis system: Single image super resolution based maximum fuzzy entropy segmentation and convolutional neural network.

Magnetic resonance imaging (MRI) images can be used to diagnose brain tumors. Thanks to these images, some methods have so far been proposed in order to distinguish between benign and malignant brain tumors. Many systems attempting to define these tumors are based on tissue analysis methods. However, various factors such as the quality of an MRI device, noisy images and low image resolution may decrease the quality of MRI images. To eliminate these problems, super resolution approaches are preferred as a complementary source for brain tumor images. The proposed method benefits from single image super resolution (SISR) and maximum fuzzy entropy segmentation (MFES) for brain tumor segmentation on an MRI image. Later, pre-trained ResNet architecture, which is a convolutional neural network (CNN) architecture, and support vector machine (SVM) are used to perform feature extraction and classification, respectively. It was observed in experimental studies that SISR displayed a higher performance in terms of brain tumor segmentation. Similarly, it displayed a higher performance in terms of classifying brain tumor regions as well as benign and malignant brain tumors. As a result, the present study indicated that SISR yielded an accuracy rate of 95% in the diagnosis of segmented brain tumors, which exceeds brain tumor segmentation using MFES without SISR by 7.5%.