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Öğe A novel deep learning approach for predicting stone-free rates post-ESWL on uncontrasted CT(Peerj Inc, 2025) Efiloglu, Ozgur; Yildirim, Muhammed; Yildirim, Kadir; Bingol, Harun; Akalin, Mustafa Kaan; Culpan, Meftun; Yildirim, AsifExtracorporeal shock wave lithotripsy (ESWL) is one of the most often employed therapy methods for managing kidney stones. In our work, we sought to assess the efficacy of the artificial intelligence model developed using non-contrast computed tomography (CT) images in predicting stone-free rates for ESWL. The main difference between this study and other studies is that it proposes an artificial intelligence-based model that predicts the success of ESWL treatment using artificial intelligence methods. Data from 910 patients who underwent ESWL between January 2016 and June 2021 were analyzed retrospectively. Since the local binary pattern (LBP) and histogram of oriented gradients (HOG) feature extraction methods gave more successful results than other methods, a new feature map was obtained using the neighborhood component analysis (NCA) dimension reduction method after combining the features obtained using these methods. Then, the reduced feature map was classified into classifiers. In conclusion, we analyzed the effect of ESWL treatment using different artificial intelligence methods and found that the prediction accuracy was 94% on average. Results were obtained from seven different convolutional neural networks (CNNs) and two textural-based models in the study. Since textural-based models achieved the highest success among these models, these models were used as the base in the proposed model. The proposed model achieved better results than nine different models used in the study. When the results obtained from the proposed hybrid model for ESWL prediction are examined, this model will guide experts in the treatment of the disease.Öğe Automatic classification of kidney CT images with relief based novel hybrid deep model(Peerj Inc, 2023) Bingol, Harun; Yildirim, Muhammed; Yildirim, Kadir; Alatas, BilalOne of the most crucial organs in the human body is the kidney. Usually, the patient does not realize the serious problems that arise in the kidneys in the early stages of the disease. Many kidney diseases can be detected and diagnosed by specialists with the help of routine computer tomography (CT) images. Early detection of kidney diseases is extremely important for the success of the treatment of the disease and for the prevention of other serious diseases. In this study, CT images of kidneys containing stones, tumors, and cysts were classified using the proposed hybrid model. Results were also obtained using pre-trained models that had been acknowledged in the literature to evaluate the effectiveness of the suggested model. The proposed model consists of 29 layers. While classifying kidney CT images, feature maps were obtained from the convolution 6 and convolution 7 layers of the proposed model, and these feature maps were combined after optimizing with the Relief method. The wide neural network classifier then classifies the optimized feature map. While the highest accuracy value obtained in eight different pre-trained models was 87.75%, this accuracy value was 99.37% in the proposed model. In addition, different performance evaluation metrics were used to measure the performance of the model. These values show that the proposed model has reached high-performance values. Therefore, the proposed approach seems promising in order to automatically and effectively classify kidney CT images.Öğe Deep learning model for automated kidney stone detection using coronal CT images(Pergamon-Elsevier Science Ltd, 2021) Yildirim, Kadir; Bozdag, Pinar Gundogan; Talo, Muhammed; Yildirim, Ozal; Karabatak, Murat; Acharya, U. RajendraKidney stones are a common complaint worldwide, causing many people to admit to emergency rooms with severe pain. Various imaging techniques are used for the diagnosis of kidney stone disease. Specialists are needed for the interpretation and full diagnosis of these images. Computer-aided diagnosis systems are the practical approaches that can be used as auxiliary tools to assist the clinicians in their diagnosis. In this study, an automated detection of kidney stone (having stone/not) using coronal computed tomography (CT) images is proposed with deep learning (DL) technique which has recently made significant progress in the field of artificial intelligence. A total of 1799 images were used by taking different cross-sectional CT images for each person. Our developed automated model showed an accuracy of 96.82% using CT images in detecting the kidney stones. We have observed that our model is able to detect accurately the kidney stones of even small size. Our developed DL model yielded superior results with a larger dataset of 433 subjects and is ready for clinical application. This study shows that recently popular DL methods can be employed to address other challenging problems in urology.Öğe Deep learning-based PI-RADS score estimation to detect prostate cancer using multiparametric magnetic resonance imaging(Pergamon-Elsevier Science Ltd, 2022) Yildirim, Kadir; Yildirim, Muhammed; Eryesil, Hasan; Talo, Muhammed; Yildirim, Ozal; Karabatak, Murat; Acharya, U. RajendraProstate cancer (PCa) is the most common type of cancer among men. Digital rectal examination and prostate-specific antigen (PSA) tests are used to diagnose the PCa accurately. Since PSA is organ-specific and not disease-specific, multiparametric magnetic resonance imaging (mpMRI) is used to reduce unnecessary biopsies. Prostate imaging reporting and data system (PI-RADS) is widely used for mpMRI scoring to detect PCa. There is low-level agreement among interpreters and also subjectivity associated with PI-RADS scoring. Hence, in this study, a hybrid model has been proposed to accurately interpret mpMRI examination and predict PI-RADS scores. In the proposed systems, feature maps of mpMR images were extracted using the MobilenetV2, Efficientnetb0, and Darknet53 architectures. Then, the feature maps obtained using these three architectures were combined. The merged feature maps are subjected to neighborhood components analysis (NCA) to eliminate redundant features. The proposed system provided 96.09% accuracy.Öğe Evaluation of the Risk of Urinary System Stone Recurrence Using Anthropometric Measurements and Lifestyle Behaviors in a Developed Artificial Intelligence Model(Mdpi, 2025) Yasar, Hikmet; Yildirim, Kadir; Karaduman, Mucahit; Kolcu, Bayram; Ezer, Mehmet; Suceken, Ferhat Yakup; Sarica, KemalBackground/Objectives: Urinary system stone disease is an important health problem both clinically and economically due to its high recurrence rates. In this study, an innovative hybrid approach based on deep learning is proposed to predict the recurrence risk of stone disease. Methods: Patient data were divided into three subsets: anthropometric measurements (Part A), derived body composition indices (Part B), and other clinical and demographic information (Part C). Each data subset was processed with autoencoder models, and low-dimensional, meaningful features were extracted. The obtained features were combined, and the classification process was performed using four different machine learning algorithms: Extreme Gradient Boosting (XGBoost), Cubic Support Vector Machines (Cubic SVM), k-Nearest Neighbor algorithm (KNN), and Decision Tree (DT). Results: According to the experimental results, the highest classification performance was obtained with the XGBoost algorithm. The suggested approach adds to the literature by offering a novel solution that makes early risk calculation for stone disease recurrence easier. It also shows how well structural feature engineering and deep representation can be integrated in clinical prediction issues. Conclusions: Prediction of the stone recurrence risk in advance is of great importance both in terms of improving the quality of life of patients and reducing the unnecessary diagnostic evaluations along with lowering treatment costs.Öğe Handedness related differences in bone mineral density in patients with osteoporosis(2013) Malkoc, Ismail; Dane, Şenol; Karatay, Saliha; Uzkeser, Hülya; Saruhan, Zeynep; Yildirim, KadirObjective Left-handedness was reported to be associated with lower bone mineral density in a recent study and also to be a risk factor for accident-related injuries, head injuries, traumatic brain injuries, sport-related injuries and bone breaks and fractures. Therefore, the bone mineral densities of 17 left-handed patients with osteoporosis were compared to those of 141 right-handed ones in 14 males and 144 females. Methods Hand preference was assessed using the Edinburgh Handedness Inventory. To measure the bone mineral density, a Hologic QDR-4500W (S/N 48403) densitometer was used. Multivariate analysis of variance was used for the statistical evaluation. Results The bone mineral densities were higher in the right-handed patients with osteoporosis than in the left-handed ones. Conclusions These results support the claim that the left-handed patients with osteoporosis had higher bone damage risk in traumas and accidents. © 2013 Elsevier GmbH. © 2014 Elsevier B.V., All rights reserved.Öğe Physical and mental effects of different radical prostatectomy techniques on urologic surgeons(Peerj Inc, 2025) Olcucu, Mahmut T.; Bolat, Mustafa S.; Yildirim, Kadir; Ozgok, Yasar; Tokas, Theodoros; Gozen, AliObjective In this web-based international survey study, we aimed to show an association between physical exhaustion and patient, relatives, and employer-related mental stress for surgeons performing open radical prostatectomy (ORP), laparoscopic radical prostatectomy (LRP), and robot-assisted laparoscopic prostatectomy (RALP). Additionally, we also aimed to compare the outcomes of three approaches. Methods We sent a ten-question survey to the urologists performing ORP, LRP, and RALP via e-mail and social media. Only fully completed surveys were included in the study analysis. We asked questions about age, the preferred surgical approach for radical prostatectomy, frequency of weekly exercise, and their possible associations with physical exhaustion and musculoskeletal complaints. Results A total of 160 urologists completed the survey. The RALP group showed a lower physical exhaustion rate and increased eye strain (p < 0.001) and p = 0.002, respectively). Although walking was the most preferred sports activity, no correlation was found between regular sport or exercise and musculoskeletal complaints (p > 0.05). Conclusion Compared to ORP and LRP, physical exhaustion was lower in the RALP technique. Although the number of participants was limited, regular exercise weakly improved physical exhaustion and musculoskeletal complaints. We believe that regular sports activities by urologists dealing with LRP and RALP will help relieve physical discomfort.Öğe THE RELATIONSHIP OF PSYCHOGENIC ERECTILE DYSFUNCTION WITH CORONAVIRUS ANXIETY IN THE COVID-19 PANDEMIC PERIOD(Carbone Editore, 2021) Ogras, Mehmet Sezai; Yildirim, KadirBackground/aim: Our aim is to investigate the relationship of psychogenic erectile dysfunction(pED) that develops during the new coronavirus disease(COVID-19) pandemic with coronavirus anxiety using the Coronavirus Anxiety Scale(CAS) and the International Index of Erectile Function-5(IIEF-5) questionnaire. Materials and methods: This study was conducted in Elazig Fethi sekin city hospital during January 2021 to March 2021. Medical history of male patients who were admitted to urology outpatient clinics were taken and physical examinations were performed. Morning serum fasting glucose, total testosterone and prolactine levels were measured. IIEF-5 questionnaire was filled by the patients. Two groups were formed as pED and control group. Both groups filled the CAS questionnaire and the results were compared statistically. Results: IIEF-5 scores were 15.86 +/- 7.53 and 24.26 +/- 0.82 in the pED group and the control group, respectively. The CAS scores were 7.53 +/- 2.02 and 0.40 +/- 0.62 in the pED group and in the control group, respectively. There was a significant difference between these findings. There was a significant negative correlation between IEF-5 scores and CAS scores. (p*=0.00) IIEF-5 scores were significantly lower in the pED group compared to the control group. (p*=0.00) CAS scores were significantly higher in the pED group compared to the control group. (p*=0.00) While there was a statistically significant difference between moderate pED and mild moderate pED in terms of CAS scores (p*=0.02, p*=0.00), there was no statistically significant difference between mild moderate pED and mild pED. (p=0.27). Conclusion: In addition to high contagiousness and mortality rates, COVID-19 causes economic burden and financial losses, leading to negative individual and global psychosocial impact and increased anxiety. Since anxiety is one of the etiological causes of pED, pED encountered during the COVID-19 pandemic is also associated with coronavirus anxiety. Psychiatric support for coronavirus anxiety should be added to pED treatment.
