Mammography is the most preferred method for breast cancer screening. In this study, computer-aided diagnosis (CAD) systems were used to improve the image quality of mammography images and to detect suspicious areas. The main contribution of this study is to reveal the optimal combination of various pre-processing algorithms to enable better interpretation and classification of mammography images because pre-processing algorithms significantly affect the accuracy of segmentation and classification methods. In this study, the effect of combinations of different preprocessing methods in differentiating benign and malignant breast lesions was investigated. All image processing algorithms used for lesion detection were used in the mini-MIAS database. In the first step, label information and pectoral muscle resulting from the acquisition of mammography images were removed. In the second step, median filter (MF), contrast limited adaptive histogram equalization (CLAHE), and unsharp masking (USM) algorithms with different combinations of the resolution and visibility of images are increased. In the third step, suspicious regions are extracted from the mammograms using the k-means clustering technique. Then, features were extracted from the obtained ROIs. Finally, feature datasets were classified as normal/abnormal, and benign/malign (two class classification) using Machine Learning algorithms. Test performance measures of the classification methods were examined. In both classifications made in the study, lower classification performance values were obtained when the CLAHE algorithm was used alone as a pre-processing method compared to other pre-processing combinations. When the median filter and unsharp masking algorithms are added to the CLAHE algorithm, the performance of the classification methods has increased. In terms of classification success, Support Vector Machines, Random Forest, and Neural Networks showed the best performance. It was found by comparing the performances of the classification methods that different preprocessing algorithms were effective in detecting the presence of breast lesions and distinguishing benign and malignant.

The concept of occupational balance in adolescents is not clear enough.The current study aims at developing a valid and reliable scale to measure occupational balance in adolescents.We conducted on...

Breast cancer is one of the most common types of cancer in women. To make a fast diagnosis, mammography images should have high contrast. Computer-assisted diagnosis (CAD) models are computer systems that help diagnose lesioned areas on medical images. The aim of this study is to examine the contribu-tion of the changes in parameter values of various pre-processing methods used to increase the visibility of mammography images and reduce the noise in the images, to the classification performance. In this study, the mini-MIAS database were used. Gaussian filter, Contrast Limited Adaptive Histogram Equalization and Fast local Laplacian filtering methods were applied as pre-processing method. In this study, two different parameter values were applied for two different image processing methods (Ⅰ. Parameter values are Gauss filter 𝜎=3, Laplacian filter 𝜎=0.6 and 𝛼=0.6; Ⅱ. Parameter values are Gauss filter 𝜎=1, Laplacian filter 𝜎=2 and 𝛼=2). In the normal-abnormal tissue classification, higher accuracy and area under the curve were obtained in the 2nd parameter values in all classification methods. As a result, it has been acquired that different parameter values of the pre-processing methods used to improve mammography images can change the success of the classification methods.

Objective: In recent years, the importance of holistic care in individuals with systemic lupus erythematosus (SLE) has been emphasized, and therefore a measurement tool that evaluates biopsychosocial impact is needed. This study was conducted to determine the validity, reliability, and responsiveness of the Cognitive Exercise Therapy Approach-Biopsychosocial Questionnaire (BETY-BQ) in individuals with SLE.Methods: Lupus Quality of Life (LupusQoL), Short Form-36 (SF-36), Health Assessment Questionnaire (HAQ), and Hospital Anxiety and Depression Scale (HADS) were used for the validity. For reliability, evaluations were repeated at 1 -week intervals and for responsiveness, the same scales were re-administered at 3-month intervals to 30 patients.Results: BETY-BQ showed moderate to high correlations with LupusQoL subparameters (rho= -0.522 to -0.718, p < 0.001), moderate to very high correlations with SF-36 subparameters (r=-0.826 to rho = 0.594, p < 0.001), high correlations with HAQ and HADS-D (rho = 0.735, p < 0.001; rho = 0.622, p < 0.001, respectively) and a moderate correlation with HADS-A (rho = 0.571, p < 0.001). In the test-retest method the correlation between the responses was very high (r = 0.989, p < 0.001). The intraclass correlation coefficient (r = 0.944, p < 0.001) and Cronbach's alpha value were found to be excellent (0.952). BETY-BQ showed moderate correlations with LupusQoL subparameters (the body image r=-0.443, p = 0.014; planning rho=-0.529, p = 0.003; fatigue r=-0.484, p = 0.007; intimate relationships rho=-0.421, p = 0.02), SF-36 social function subparameter (rho=-0.427, p = 0.019) and anxiety and depression subparameter of HADS (rho = 0.418, p = 0.021; r = 0.507, p = 0.004, respectively).Conclusion: BETY-BQ which was found as a valid, reliable, and responsive scale in individuals with SLE, was presented to healthcare professionals for use in clinical and research studies as a scale that can be preferred in the evaluation of biopsychosocial status of individuals with SLE.

Introduction: Medial compartment knee osteoarthritis (OA) is characterized by varus deformity. A medial open-wedge high tibial osteotomy (MOWHTO), frequently invoked in the treatment of this deformity, affects the knee as well as the ankle joints. This study aims to evaluate the radiological and clinical effects of a MOWHTO on the ankle joint. Materials and Methods: A retrospective analysis was conducted with data from 110 patients (mean age: 52 years; 74.5% female) who underwent a MOWHTO between 2020 and 2023. Radiographic assessments were conducted both preoperatively and one year after surgery using full-length weight-bearing radiographs. The measurements included several alignment parameters such as the hip–knee–ankle angle (HKA), medial proximal tibial angle (MPTA), joint line convergence angle (JLCA), lateral distal tibial angle (LDTA), and talar tilt. Clinical outcomes were assessed using the Lysholm knee score and the American Orthopedic Foot and Ankle Society (AOFAS) ankle score. Results: While changes in the LDTA demonstrated a small effect size (d = 0.225), moderate-to-large effect sizes were observed in key alignment parameters (MPTA (d = 0.838), the JLCA (d = 0.798), and talar tilt (d = 0.752)), all of which showed statistically significant differences indicative of a correction in the joint alignment of potential clinical significance. Median Lysholm and AOFAS scores at one year were 90 and 100, respectively, indicating favorable clinical outcomes. No significant difference in outcomes was observed based on the amount of correction. Conclusions: An MOWHTO not only restores knee alignment but also significantly improves ankle alignment in the coronal plane. These findings suggest that an MOWHTO is associated with the restoration of knee alignment and with improvements in ankle alignment in the coronal plane.

Multiple comparison methods are applied to control the type I error rate at the nominal level. In this study, we investigate the performance of multiple comparison methods after analysis of variance (ANOVA) is implemented under different conditions. We include Bonferroni, Holm, Hochberg, Hommel, Benjamini-Hochberg (BH), and Benjamini-Yekutieli (BY) correction methods. Monte-Carlo simulation study is applied to assess their performances under different patterns, including sample size and group number combinations. Wide inferences are drawn on considered methods, and suggestions are provided for selecting appropriate methods. Moreover, the methods are implemented on three different types of real-life data sets to emphasize the importance of these correction methods in the research.