Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/11727/4809
Browse
6 results
Search Results
Item Comparison Of Efficacy Of Oral Versus Intra-Articular Corticosteroid Application In The Treatment Of Frozen Shoulder: An Experimental Study In Rats(2022) Cinar, Bekir Murat; Battal, Vahit Erdal; Bal, Nebil; Guler, Umit Ozgur; Beyaz, Salih; 35234132Objective: The aim of this study was to compare clinical and histopathological effects of oral versus intraarticular corticosteroid application in a rat model of frozen shoulder. Methods: In this study, eighty adult Sprague-Dawley rats were used. The animals were divided into 5 equal groups. The frozen shoulder model was created by immobilizing animals' shoulders with internal fixation with sutures for 8 weeks. At the 8th week, sham(n: 16) and control (n: 16) groups were sacrificed to collect data for healthy and affected shoulders. Also, at the 8th week, 50 mg/kg methylprednisolone was started for the oral treatment group, and a single dose of 0.5mg/kg triamcinolone acetonide was injected for the intraarticular treatment group. The effect of additional steroid treatment was expected for 2 weeks, then all remaining treatment and natural course groups were sacrificed on the 10th week. Results: After sacrification, specimens taken as "en bloc" scapulothoracic disarticulation were randomly divided into two groups for a range of motion measurement and histopathological examination. The control (frozen shoulder model) group's shoulder range of motion in all directions was lower than the sham (healthy) group (P < 0.01). Natural course and intraarticular steroid groups, compared to the frozen shoulder model showed a significant increase in the direction of abduction (P < 0.05). Also, it was found for treatment groups that in all directions the range of motion was not as good as the healthy values (P < 0.01). The intraarticular treatment group showed higher degrees of abduction compared to the natural course and oral steroid treatment groups (P < 0.01). Oral steroid treatment group's range of motion was not significantly better than the disease model and had no superiority to the natural course group (P > 0.05). Histopathologically, no statistically significant difference was observed between the groups for signs of frozen shoulder which was found in the immobilized group (P > 0.05). Histopathologically, immobilization was found to cause thickening of the capsule that cannot be resolved by treatment. (P < 0.05). Conclusion: In frozen shoulder disease, intraarticular steroid injection seems to be superior in increasing the range of motion than oral steroid treatment.Item Comparison of Different Machine Learning Approaches to Detect Femoral Neck Fractures in X-Ray Images(2021) Acici, Koray; Sumer, Emre; Beyaz, Salih; 0000-0002-3821-6419; 0000-0001-8502-9184; 0000-0002-5788-5116; AGA-5711-2022; K-8820-2019Femoral neck fractures are a serious health problem, especially in the elderly population. Misdiagnosis leads to improper treatment and adversely affects the quality of life of the patients. On the other hand, when looking from the perspective of orthopedic surgeons, their workload increases during the pandemic, and the rates of correct diagnosis may decrease with fatigue. Therefore, it becomes essential to help healthcare professionals diagnose correctly and facilitate treatment planning. The main purpose of this study is to develop a framework to detect fractured femoral necks in PXRs (Pelvic X-ray, Pelvic Radiographs) while also researching how different machine learning approaches affect different data distributions. Conventional, LBP (Local Binary Patterns), and HOG (Histogram of Gradients) features were extracted manually from gray-level images to feed the canonical machine learning classifiers. Gray-level and three-channel images were used as inputs to extract the features automatically by CNNs (Convolutional Neural Network). LSTMs (Long Short-Term Memory) and BILSTMs (Bidirectional Long Short-Term Memory) were fed by automatically extracted features. Metaheuristic optimization algorithms, GA (Genetic Algorithm) and PSO (Particle Swarm Optimization), were utilized to optimize hyper-parameters such as the number of the feature maps and the size of the filters in the convolutional layers of the CNN architecture. The majority voting was applied to the results of the different classifiers. For the imbalanced dataset, the best performance was achieved by the 2-layer LSTM architecture that used features extracted from the fifth max-pooling layer of the CNN architecture optimized by GA. For the balanced dataset, the best performance was obtained by the CNN architecture optimized by PSO in terms of the Kappa evaluation metric. Although metaheuristic optimization algorithms such as GA and PSO do not guarantee the optimal solution, they can improve the performance on a not extremely imbalanced dataset especially in terms of sensitivity and Kappa evaluation metrics. On the other hand, for a balanced dataset, more reliable results can be obtained without using metaheuristic optimization algorithms but including them can result in an acceptable agreement in terms of the Kappa metric.Item The effect of a new universal laser aiming device in C-arm fluoroscopy on the technician's accuracy(2020) Beyaz, Salih; Kurtuldu, Huseyin; 0000-0002-5788-5116; 32160486; K-8820-2019Objectives: This study aims to introduce a new low-cost universal laser aiming device (LAD) that can be used in existing C-arm fluoroscopy devices, independent of brand and model. and to determine whether this new universal LAD improves technician accuracy in locating the desired region at the midpoint of the fluoroscopic image. Materials and methods: A low-priced universal LAD that is compatible with existing 12-inch C-arm fluoroscopy devices was designed. Eight radiology technicians with varied levels of experience in C-arm fluoroscopy participated in the study. A 12mm cortical screw with a diameter of 3.5 mm was placed on proximal, diaphyseal. and distal points of femur, tibia, and humerus bones in the anteroposterior plane on L3 vertebrae and the left pubis arm in the pelvis bone model. Technicians were asked to align each screw in the image center 10 times from a distance of 30 cm in the anterolateral plane, first without the LAD and then with the LAD. The distance of the screw head to the center point was measured from the 3,520 images with the help of medical viewer software based on the X- and Y-axis. Results: Each fluoroscopic image was divided into 48 equal parts and the length of a part was taken as one unit for distance measurements. The compliance between technicians without the LAD was 0.347 (95% confidence interval [CI]: 0.208-0.47, p=0.001) and with the LAD was 0.687 (95% CI: 0.621-0.741, p=0.001). The distance between the screw head and the center of the image without the LAD was 19.0=9.8 for technicians with more than 10 years of experience and 28.0 +/- 12.9 for those with less than 10 years of experience. This difference was statistically significant (p=0.001). When the LAD was used, the difference between the less experienced (3.1 +/- 1.5) and more experienced (3.3 +/- 2.0) technicians was statistically reduced, along with the distance (p=0.033). Conclusion: The use of the LAD with C-arm fluoroscopy appears to be successful in helping technicians capture the desired point in the center of the fluoroscopic image. The use of the LAD reduces the experience gap between technicians.Item Femoral neck fracture detection in X-ray images using deep learning and genetic algorithm approaches(2020) Beyaz, Salih; Acici, Koray; Sumer, Emre; 0000-0002-5788-5116; 32584712; K-8820-2019Objectives: This study aims to detect frontal pelvic radiograph femoral neck fracture using deep learning techniques. Patients and methods: This retrospective study was conducted between January 2013 and January 2018. A total of 234 frontal pelvic X-ray images collected from 65 patients (32 males, 33 females; mean age 74.9 years; range, 33 to 89 years) were augmented to 2106 images to achieve a satisfactory dataset. A total of 1,341 images were fractured femoral necks while 765 were non-fractured ones. The proposed convolutional neural network (CNN) architecture contained five blocks, each containing a convolutional layer, batch normalization layer, rectified linear unit, and maximum pooling layer. After the last block, a dropout layer existed with a probability of 0.5. The last three layers of the architecture were a fully connected layer of two classes, a softmax layer and a classification layer that computes cross entropy loss. The training process was terminated after 50 epochs and an Adam Optimizer was used. Learning rate was dropped by a factor of 0.5 on every five epochs. To reduce overfitting, regularization term was added to the weights of the loss function. The training process was repeated for pixel sizes 50x50, 100x100, 200x200, and 400x400. The genetic algorithm (GA) approach was employed to optimize the hyperparameters of the CNN architecture and to minimize the error after testing the model created by the CNN architecture in the training phase. Results: Performance in terms of sensitivity, specificity, accuracy, F1 score, and Cohen's kappa coefficient were evaluated using five-fold cross validation tests. Best performance was obtained when cropped images were rescaled to 50x50 pixels. The kappa metric showed more reliable classifier performance when 50x50 pixels image size was used to feed the CNN. The classifier performance was more reliable according to other image sizes. Sensitivity and specificity rates were computed to be 83% and 73%, respectively. With the inclusion of the GA, this rate increased by 1.6%. The detection rate of fractured bones was found to be 83%. A kappa coefficient of 55% was obtained, indicating an acceptable agreement. Conclusion: This experimental study utilized deep learning techniques in the detection of bone fractures in radiography. Although the dataset was unbalanced, the results can be considered promising. It was observed that use of smaller image size decreases computational cost and provides better results according to evaluation metrics.Item A brief history of artificial intelligence and robotic surgery in orthopedics & traumatology and future expectations(2020) Beyaz, Salih; 0000-0002-5788-5116; 32962606; K-8820-2019Recently. the rate of the production and renewal of information makes it almost impossible to be updated. It is quite difficult to process and interpret large amounts of data by human beings. Unlimited memory capacities. learning abilities, artificial intelligence (AI) applications, and robotic surgery techniques cause orthopedic surgeons to be concerned about losing their jobs. The idea of AI. which was first introduced in 1956. has evolved over time by revealing deep learning and evolutionary plexus that can mimic the human neuron cell. Image processing is the leading improvement in developed algorithms. Theoretically. these algorithms appear to be quite successful in interpreting medical images and orthopedic decision support systems for preoperative evaluation. Robotic surgeons have emerged as significant competitors in carrying out the taken decisions. The first robotic applications of orthopedic surgery started in 1992 with the ROBODOC system. Applications started with hip arthroplasty continued with knee arthroplasty. Publications indicate that problems such as blood loss and infection caused by the long operation time in the early stages have been overcome in time with the help of learning systems. Comparative studies conducted with humans indicate that robots are better than humans in providing limb lengthening, patient satisfaction, and cost. As in all new technologies, the developments in both AI applications and robotics surgery indicate that technology is in favor in terms of cost/benefit analyses. Although studies indicate that new technologies are more successful than humans, the replacement of technology with experience and long-term results with traditional methods will not be observed in the near future.Item Factors affecting lifespan following below-knee amputation in diabetic patients(2017) Beyaz, Salih; Guler, Umit Ozgur; Bagir, Gulay Simsek; 0000-0002-5788-5116; 0000-0002-5375-635X; 0000-0002-5375-635X; 28865844; K-8820-2019; O-7590-2017; AAJ-4844-2021Introduction: Untreatable foot problems in diabetics may require lower extremity amputation, which has a high level of patient mortality. This high mortality rate is worse than most malignancies. The present study aimed to identify parameters that can be used to estimate survival in DM patients undergoing below-knee amputations for diabetic foot problems. Materials and methods: A total of 470 patients (299 males, 171 females) with a mean age of 64.32 years who underwent below-knee amputation for diabetic foot problems between 2004 and 2014 were enrolled in the study. The length of time from the operation to time of death was recorded in days. Patient details were obtained, including age during surgery, BMI, oral antidiabetic and insulin usage, dialysis therapy history, lower extremity endovascular intervention, previous amputation at the same extremity, the need for stump revision surgery during follow-up, and above-knee amputation at the same site. Biochemical test results of pre-operative HbAl c, ESR, and levels of CRP, BUN, and creatinine were also obtained. Results: A total of 333 patients (70.9%) died and 137 (29.1%) survived post-surgery. Survival rates were 90% in the first 7 days, 84% in the first 30 days, and 64% after the first year. Patient median life expectancy post-surgery was 930 106 days. Hemodialysis treatment (p = 0.001), endovascular intervention (p = 0.04), sex (p = 0.004), age (p = 0.001), BUN level (p = 0.001), and duration of insulin use (p = 0.003) were shown to be effective predictors of mortality. Conclusions: Life expectancy is low (<3 years) in DM patients requiring below-knee amputations for untreatable foot problems. Survival could be predicted by duration of insulin use, age, sex, and renal insufficiency. Level of evidence: Level IV, Therapeutic study. (C) 2017 Turkish Association of Orthopaedics and Traumatology. Publishing services by Elsevier B.V.