Sürdürülebilir Kalkınma Amaçları
Yayınlar
- Editöre MektupAçık Erişim2026Reply to the Letter to the Editor: Assessing the Predictive Value of Kolmogorov–Arnold Networks for the No–Reflow Phenomenon in ST–Segment Elevation Myocardial Infarction: A Comparative Machine Learning Study; [Editöre Mektup Yanıtı: ST Segment Yükselmesi Miyokard Enfarktüsünde No–Reflow Fenomeni için Kolmogorov–Arnold Ağlarının Öngörü Değerinin Değerlendirilmesi: Karşılaştırmalı Makine Öğrenimi Çalışması]
Taşolar, Hakan; Bayramoğlu, Adil; Günen, Mehmet Akif; Levent, Sümeyye; Güral, Yunus; Halisdemir, Nurhan
- MakaleAçık Erişim2026A Deep Learning–Based Framework for Automatic Determination of Developmental Dysplasia of the Hip from Graf Angles
Taşolar, Sevgi; Günen, Mehmet Akif; Sığırcı, Ahmet; Taşolar, Hakan
Developmental dysplasia of the hip (DDH) is a common neonatal condition that necessitates early diagnosis to ensure effective treatment. The traditional Graf method, while widely used for evaluating infant hips via ultrasound, is limited by operator dependency and measurement variability. This research has proposed a framework using deep learning network, morphological operation and local maxima method to diagnose DDH in newborns using ultrasound images. The method utilizes DeepLabv3 + for image segmentation, evaluating multiple backbone architectures (ResNet50, InceptionResNetV2, MobilenetV2, and Xception) to identify the region of interest accurately. Local maxima method was used to determine the extremum points of the line defining the Graf angles. Denoising filters, including mean, median, and Wiener, are applied to determine local maxima points accurately. The evaluation comprises two stages: first, assessing the performance of DeepLabv3 + backbones in producing masks for Graf angles determination, and second, comparing the angles obtained through proposed framework with those determined by expert radiologists. Comparative analysis demonstrates that MobileNetV2 (94.64 accuracy, 86.99 Cohen’s kappa, 94.31 F-score) surpasses other models in segmentation accuracy and measurement reliability. This conclusion is backed by key performance metrics such as accuracy, IoU, PSNR, F-score, SSIM, Cohen’s kappa, as well as by the intraclass correlation coefficient and Bland–Altman analyses. The proposed framework shows considerable promise in automating hip ultrasound analysis for DDH diagnosis, minimizing operator dependency while enhancing measurement consistency.
- MakaleAçık Erişim2026Assessing the Predictive Value of Kolmogorov–Arnold Networks for the No–Reflow Phenomenon in ST–Segment Elevation Myocardial Infarction: A Comparative Machine Learning Study; [ST Segment Yükselmeli Miyokard Enfarktüsünde No–Reflow Fenomeni için Kolmogorov–Arnold Ağlarının Öngörü Değerinin Değerlendirilmesi: Karşılaştırmalı Makine Öğrenimi Çalışması]
Taşolar, Hakan; Bayramoğlu, Adil; Günen, Mehmet Akif; Levent, Sümeyye; Güral, Yunus; Halisdemir, Nurhan
Objective: The no-reflow phenomenon in ST-segment elevation myocardial infarction (STEMI) is a significant clinical issue associated with poor cardiovascular outcomes. This study aimed to develop and compare multiple supervised machine learning algorithms, including the recently introduced Kolmogorov-Arnold Network (KAN), to predict the occurrence of the no-reflow phenomenon in patients with STEMI undergoing primary percutaneous coronary intervention (PCI). Method: This prospective, single-center study included 890 consecutive STEMI patients undergoing primary PCI. The Synthetic Minority Over-sampling Technique (SMOTE) was utilized to address class imbalance during training. Feature selection using analysis of variance (ANOVA) F-statistics and validation of feature independence (Variance Inflation Factor [VIF] < 5) identified ejection fraction (EF), baseline troponin level, stent length, B-type natriuretic peptide (BNP) level, and total ischemic time as the most influential predictors. Results: The KAN and Extreme Gradient Boosting (XGBoost) models achieved the highest predictive accuracy (area under the curve > 0.98, F1 > 0.95), outperforming traditional models such as logistic regression and decision tree classifiers (DeLong test, P < 0.001). Feature selection improved efficiency and reduced runtime by 20–40%, while Shapley Additive exPlanations-based (SHAP-based) explainability confirmed that the predictions were physiologically consistent: higher EF and lower BNP reduced the probability of no-reflow, whereas longer stent length and ischemic time increased it. The superior performance of KAN and XGBoost underscores the importance of modeling nonlinear relationships and multidimensional interactions among clinical, laboratory, and procedural variables. Conclusion: These findings suggest that KAN may serve as a reliable analytical framework for exploring complex cardiovascular outcomes. However, further multicenter and externally validated studies are needed to confirm its generalizability and potential role in clinical risk assessment.