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    Enhancing knee osteoarthritis detection with AI, image denoising, and optimized classification methods and the importance of physical therapy methods
    (Peerj Inc, 2025) Bugday, Burak; Bingol, Harun; Yildirim, Muhammed; Alatas, Bilal
    Osteoarthritis (OA) is considered one of the most challenging arthritic disorders due to its high disease burden and lack of effective treatment options that can change the course of the disease. Knee osteoarthritis (KOA) reduces people's quality of life and shortens their daily activities. Therefore, early detection of KOA dramatically impacts patients' quality of life. This study developed an artificial intelligence-supported system to detect KOA. In the developed system, firstly, the images in the original dataset were denoised with a Gaussian filter. Then, feature maps were extracted from both the original and Gaussian applied datasets with the DenseNet201 selected from eight different pre-trained models, and these two feature maps were concatenated. In this way, it is aimed to bring together different features of the same image. Then, feature selection was made using the neighborhood component analysis (NCA) method for the developed system to produce more successful results, and the optimized feature map was classified into six different classifiers. As a result, a high accuracy rate of 85% was achieved in the proposed model. This value is promising for the automatic diagnosis of KOA with computer-aided systems. As a result, a high accuracy rate of 85% was achieved in the developed system of the support vector machine (SVM) classifier. The proposed model was more successful than the other models used in the study.
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    Impact of resistance exercise and diet on physical activity, sleep, and fatigue in obese individuals: a randomized controlled trial
    (Bmc, 2025) Bugday, Burak; Celik, Alara Livanur; Safran, Elif Esma; Sevgin, Omer
    Background Obesity is a global health challenge linked to reduced physical activity, poor sleep, and increased fatigue. While diet is central to weight management, resistance training may offer additional benefits, but its combined impact on sleep and activity patterns in individuals with obesity remains unclear. This study aimed to compare the effects of a 12-week resistance training program combined with calorie-restricted dietary intervention (Diet + RT) versus diet alone on self-reported physical activity levels, sleep quality, fatigue, body weight, and waist circumference in individuals with obesity. Methods In this randomized controlled trial, 40 individuals with obesity were assigned to either a calorie-restricted Diet group or a Diet + RT group for 12 weeks. The exercise program targeted major muscle groups at moderate intensity, performed three times per week under professional supervision. Outcomes included the International Physical Activity Questionnaire (IPAQ), Pittsburgh Sleep Quality Index (PSQI), Fatigue Severity Scale (FSS), body weight, and waist circumference, measured at baseline, mid-intervention, and post-intervention. Data were analyzed using repeated-measures ANOVA and chi-square tests. Results Groups were comparable at baseline (all p > 0.05). Significant time x group interactions were found for body weight (F = 25.83), waist circumference (F = 27.38), sleep quality (F = 22.19), and fatigue severity (F = 53.94) (all p < 0.001), favoring the combined intervention. Self-reported physical activity levels, measured via the IPAQ, also differed significantly between groups at baseline (chi(2) = 8.44), mid-intervention (chi(2) = 16.41), and post-intervention (chi(2) = 14.40) (all p < 0.01). Conclusions Diet + RT yields superior improvements in self-reported physical activity, sleep quality, body weight, fatigue, and waist circumference compared to diet alone in individuals with obesity.