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    Forecasting Stone-Free Status Following Percutaneous Nephrolithotomy Utilizing Explainable Machine Learning
    (Mdpi, 2026) Cicek, Resul; Topcu, Ibrahim; Dural, Bulut; Balikci Cicek, Ipek; Yilmaz, Murat; Colak, Cemil
    Background: This study aimed to create and evaluate explainable machine learning models for forecasting postoperative stone-free status following percutaneous nephrolithotomy (PNL) utilizing a substantial clinical cohort. Methods: This retrospective single-center analysis encompassed 2144 adult patients who received PNL from 2010 to 2024. We employed clinical, radiographic, stone-related, and surgical data to train four supervised machine learning models: Extreme Gradient Boosting (XGBoost), Random Forest, Light Gradient Boosting Machine (LightGBM), and Adaptive Boosting (AdaBoost). We used the Synthetic Minority Oversampling Technique exclusively on the training set to fix the class imbalance. We assessed the model's accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (ROC-AUC) to see how well it worked. SHapley Additive exPlanations (SHAP) were used to measure explainability. Results: The total stone-free rate was 84.8%. XGBoost had the best predictive performance of the models tested, with an accuracy of 0.916 and a ROC-AUC of 0.975. LightGBM was close behind. Random Forest and AdaBoost had relatively inferior performance. SHAP analysis identified anatomical anomalies as demonstrated the strongest association with stone-free outcomes. The size of the access sheath and the number of stones were next. Other parameters that were identified by SHAP as important contributors to model predictions were the placement of the stone, Guy's Stone Score, the length of the operation, and the density of the stone. These feature associations demonstrated clinical coherence with established knowledge in surgical practice. Conclusions: Explainable machine learning algorithms, especially XGBoost, can accurately predict stone-free outcomes following PNL in a way that makes sense to doctors. The incorporation of SHAP improves transparency and facilitates the prospective application of these models as decision-support instruments in personalized surgical planning.
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    Open Source Web Based Software for Random Assignment/Allocation Methods in Data Processing
    (Ieee, 2019) Arslan, A. Kadir; Balikci Cicek, Ipek; Colak, Cemil
    In this study, it is aimed to develop a user-friendly open-source web-based software which enables the assignment of the subjects included in the scientific research to the groups with equal probability. An open source R package, Shiny, is used to develop the recommended web tool. In the developed software, one of the required equilibrium methods; random allocation rule, truncated binomial design, maximal procedure design, complete randomization methods; complete randomization design, blocking methods; permuted block randomization with random block constellation, the Hadamard randomization, adaptive methods; the big stick design, Efron's biased coin design, Wei's urn design, generalized biased coin design, Chen's biased coin design are included. A random allocation rule, one of the random assignment methods, is applied to a hypothetical data set where the sample size is 140 and the number of groups are two. As a result, in the first group, a random assignment was made in such a way that the number of samples are 70 and the number of samples in the second group are 70. According to the hypothetical data set findings, the developed easily assigns the subjects to the study groups by using random assignment methods. Therefore, it is stated that it easily solved a significant bias problem in scientific studies. In the following stages of the study, the scope of the software can be expanded with the addition of techniques comparing the results of random assignment methods.
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    The February 2023 Turkey Earthquake and Its Impact on Asthma Exacerbations and Healthcare Utilization
    (Mdpi, 2025) Kaya, Saltuk Bugra; Balikci Cicek, Ipek; Kucukakcali, Zeynep
    Background and Objectives: To assess the impact of the February 2023 earthquake in Turkey on asthma patients' clinical outcomes and healthcare use. Materials and Methods: This retrospective, single-center study included 280 asthma patients followed at an outpatient clinic between January 2022 and December 2023. Clinical assessments included physical examinations, pulmonary function tests (PFTs), chest X-rays, and, when indicated, skin prick tests (SPTs) for aeroallergen sensitivity. Results: Following the earthquake, outpatient visits for asthma significantly increased from 82 to 198 patients (p < 0.001), and hospitalizations due to asthma attacks rose markedly (p < 0.001). While respiratory function parameters did not differ significantly between periods, there was a significant increase in the number of patients requiring advanced treatment (p = 0.037). Concurrently, air quality deteriorated, with substantial increases in particulate matter (PM10) and sulfur dioxide (SO2) levels recorded post-earthquake. Conclusions: The earthquake was associated with a significant rise in asthma exacerbations and healthcare utilization, likely driven by environmental pollution, poor living conditions, and disruptions in healthcare services. Disaster preparedness is key to protecting respiratory health after major earthquakes.
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    Ultrasonographic Assessment of the Diaphragm and the Effects of Smoking on Respiratory Function in Individuals Attending a Smoking Cessation Center
    (Mdpi, 2026) Utus, Ahmet; Ozyilmaz, Semiramis; Karatas, Turgay; Dag, Nurullah; Ural, Gurkan; Balikci Cicek, Ipek; Kilic, Murat
    Background: Smoking adversely affects pulmonary function and systemic health; however, its impact on diaphragm muscle morphology and its relationship with functional capacity and psychosocial outcomes in individuals without clinically diagnosed respiratory disease remain unclear. This study aimed to evaluate diaphragm muscle thickness in smokers and to investigate its associations with pulmonary function, functional capacity, sleep quality, and depression. Methods: This cross-sectional observational study included 20 smokers and 20 age-matched never-smokers. Pulmonary function was assessed using spirometry. Functional capacity was evaluated with the 6-Minute Walk Test (6 MWT) and the 30 s sit-to-stand test (30 s STST). Sleep quality and depression were assessed using the Pittsburgh Sleep Quality Index (PSQI) and the Beck Depression Inventory (BDI). Inspiratory and expiratory diaphragm muscle thicknesses were measured by ultrasonography. Between-group comparisons and correlation analyses were performed. Results: Smokers exhibited significant impairments in all assessed parameters except expiratory diaphragm thickness compared with controls (p < 0.05). Large to very large effect sizes were observed for FEV1, FEF25-75%, functional capacity, and inspiratory diaphragm thickness. Inspiratory diaphragm thickness showed moderate to strong positive correlations with pulmonary function parameters and a very strong positive correlation with functional capacity, while strong negative correlations were observed with sleep quality and depression (p < 0.05). Smoking duration was strongly associated with poorer functional and psychosocial outcomes. Conclusions: Smoking is associated with early and multidimensional impairments in diaphragm muscle morphology, pulmonary function, functional capacity, and psychosocial status, even in individuals without overt respiratory disease. Reduced inspiratory diaphragm thickness may represent an early and clinically meaningful marker of smoking-related respiratory muscle dysfunction.