WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Deep-Learning Detection of Open-Apex Teeth on Panoramic Radiographs Using YOLO Models(Springer, 2025-12-23) Edik, Merve; Celebi, Fatma; Cukurluoglu, AykaganObjectivesThe use of deep learning in detecting teeth with open apices can prevent the need for additional radiographs for patients. The presented study aims to detect open-apex teeth using You Only Look Once (YOLO)-based deep learning models and compare these models.MethodsA total of 966 panoramic radiographs were included in the study. Open-apex teeth in panoramic radiographs were labeled. During the labeling process, they were divided into 6 classes in the maxilla and mandible, namely incisors, premolars, and molars. AI models YOLOv3, YOLOv4, and YOLOv5 were used. To evaluate the performance of the three detection models, both overall and separately for each class in the test dataset, precision, recall, average precision (mAP), and F1 score were calculated.ResultsYOLOv4 achieved the highest overall performance with a mean average precision (mAP) of 87.84% at IoU (Intersection over Union) 0.5 (mAP@0.5), followed by YOLOv5 with 85.6%, and YOLOv3 with 84.46%. Regarding recall, YOLOv4 also led with 90%, while both YOLOv3 and YOLOv5 reached 89%. Moreover, the F1 score was the highest for YOLOv4 (0.87), followed by YOLOv3 (0.86) and YOLOv5 (0.85).ConclusionsIn this study, YOLOv3, YOLOv4, and YOLOv5 were evaluated for the detection of open-apex teeth, and their mAP, recall, and F1 scores exceeded 84%. Deep learning-based systems can provide faster and more accurate results in the detection of open-apex teeth. This may help reduce the need for additional radiographs from patients and aid dentists by saving time.Article Citation - WoS: 1Citation - Scopus: 1Prediction of the Diffusible Hydrogen Concentration After Electrochemical Charging Utilizing Artificial Intelligence(IOP Publishing Ltd, 2025-09-01) Sivesoglu, Abdurrahman; Li, Yang; Bal, BurakThe concentration of diffusible hydrogen in a material is of high importance as it helps to predict the hydrogen embrittlement effect in the material, and the amount of mechanical properties' degradation after reaching a critical concentration. Despite that, a simple experimental setup is not available to measure hydrogen concentration at service. In this paper, a multi-layer perceptron (MLP) model is developed using weight initialization, which can estimate the diffusible hydrogen concentration of Face-Centred-Cubic (FCC) metals after electrochemical charging. The input properties of the model include the electrochemical charging parameters of current density, temperature, and charging time as well as the grain size of the specimen. The MLP model with and without the weight initialization was validated and tested with unseen test dataset. The model in both cases showed an excellent predictive performance with a higher accuracy and faster convergence when using weight initialization. A linear correlation of 89% between the experimental and predicted hydrogen concentration was observed. This demonstrates that for the family of FCC metals under electrochemical charging, the estimation of diffusible hydrogen concentration is a feasible path for material safety design analysis.Article Citation - WoS: 3Citation - Scopus: 4Prediction of Biomechanical Properties of Ex Vivo Human Femoral Cortical Bone Using Raman Spectroscopy and Machine Learning Algorithms(Elsevier, 2025-09) Unal, Mustafa; Unlu, Ramazan; Uppuganti, Sasidhar; Nyman, Jeffry S.This study applied Raman spectroscopy (RS) to ex vivo human cadaveric femoral mid-diaphysis cortical bone specimens (n = 118 donors; age range 21-101 years) to predict fracture toughness properties via machine learning (ML) models. Spectral features, together with demographic variables (age, sex) and structural parameters (cortical porosity, volumetric bone mineral density), were fed into support vector regression (SVR), extreme tree regression (ETR), extreme gradient boosting (XGB), and ensemble models to predict fracture-toughness metrics such as crack-initiation toughness (Kinit) and energy-to-fracture (J-integral). Feature selection was based on Raman-derived mineral and organic matrix parameters, such as nu 1Phosphate (PO4)/CH2-wag, nu 1PO4/ Amide I, and others, to capture the complex composition of bone. Our results indicate that ensemble models consistently outperformed individual models, with the best performance for crack initiation toughness (Kinit) prediction being achieved using the ensemble approach. This yielded a coefficient of determination (R2) of 0.623, root-mean squared error (RMSE) of 1.320, mean absolute error (MAE) of 1.015, and mean percentage absolute error (MAPE) of 0.134. For prediction of the overall energy to propagate a crack (J-integral), the XGB model achieved an R2 of 0.737, RMSE of 2.634, MAE of 2.283, and MAPE of 0.240. This study highlights the importance of incorporating mineral quality properties (MP) and organic matrix properties (OMP) for enhanced prediction accuracy. This work represents the first-ever study combining Raman spectroscopy with other clinical and structural features to predict fracture toughness of human cortical bone, demonstrating the potential of artificial intelligence (AI) and ML in advancing bone research. Future studies could focus on larger datasets and more advanced modeling techniques to further improve predictive capabilities.Editorial Editorial Market-Based Distributed Energy Resources Operation for Future Power Systems(Frontiers Media S.A., 2022-12-13) Onen, Ahmet; Jung, Jaesung; Guerrero, Josep M. M.; Lee, Chul-Ho; Hossain, Md Alamgir