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    Artificial Intelligence-Based Detection and Numbering of Dental Implants on Panoramic Radiographs
    (Wiley, 2025) Balel, Yunus; Sagtas, Kaan; Teke, Fatih; Kurt, Mehmet Ali
    ObjectivesThis study aimed to develop an artificial intelligence (AI)-based deep learning model for the detection and numbering of dental implants in panoramic radiographs. The novelty of this model lies in its ability to both detect and number implants, offering improvements in clinical decision support for dental implantology.Materials and MethodsA retrospective dataset of 32 585 panoramic radiographs, collected from patients at Sivas Cumhuriyet University between 2014 and 2024, was utilized. Two deep-learning models were trained using the YOLOv8 algorithm. The first model classified the regions of the jaw to number the teeth and identify implant regions, while the second model performed implant segmentation. Performance metrics including precision, recall, and F1-score were used to evaluate the model's effectiveness.ResultsThe implant segmentation model achieved a precision of 91.4%, recall of 90.5%, and an F1-score of 93.1%. For the implant-numbering task, precision ranged from 0.94 to 0.981, recall from 0.895 to 0.956, and F1-scores from 0.917 to 0.966 across various jaw regions. The analysis revealed that implants were most frequently located in the maxillary posterior region.ConclusionsThe AI model demonstrated high accuracy in detecting and numbering dental implants in panoramic radiographs. This technology offers the potential to reduce clinicians' workload and improve diagnostic accuracy in dental implantology. Further validation across more diverse datasets is recommended to enhance its clinical applicability.Clinical RelevanceThis AI model could revolutionize dental implant detection and classification, providing fast, objective analyses to support clinical decision-making in dental practices.
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    Geochemical and Pb-Sr-Nd isotopic composition of the ultrapotassic volcanic rocks from the extension-related Camardi-Ulukisla basin, Nigde Province, Central Anatolia, Turkey
    (PERGAMON-ELSEVIER SCIENCE LTD, 2006) Alpaslan, Musa; Boztug, Durmus; Frei, Robert; Temel, Abidin; Kurt, Mehmet Ali
    Major, trace element and Sr-Nd-Pb isotope data are presented for the ultrapotassic lavas and dykes from the Late Cretaceous-Early Tertiary Ulukisla Basin in the Central Anatolia. All samples have geochemical characteristics belonging to Group III ultrapotassic rocks (Foley, S.F., Venturelli, G., Green, D.H., Toscani, L., 1987, The ultrapotassic rocks: characteristics, classification and constraints for petrogenetic models, Earth Science Reviews, 24, 81-134.). These rocks have unusually high contents of large-ion-lithophile elements (LILE) (e.g. Ba up to 5900 ppm, K2O up to 8 wt% in lava and 10 wt% in dykes). Negative Nb and Ti anomalies and LREE enrichments relative to HREE on chondrite normalized trace and rare earth element patterns indicate that subduction related material is present in the mantle source region. Their high initial Sr-87/Sr-86 (0.70798-0.70917) and low Nd-143/Nd-144 (0,512109-0,512239) ratios suggest that they originated from an enriched lithospheric mantle source with low Sm/Nd ratios. The elevated Pb-207/Ph-206 (15.743-15.797), low Nd-143/Nd-144 ratios and geochemical features such as low Nb/La and elevated Ce/Sr ratios may reflect the involvement of sediments as a metasomatic agent for the source region. The steep trend on the Pb-207/Ph-204 vs Pb-206/Ph-204 diagram also imply that the metasomatic component represents recycled continent-derived material in the source region. Integration of the geochemistry with regional and local geological data suggest that the ultrapotassic volcanic rocks from the Camardi-Ulukisla basin were derived from a lithospheric mantle material in a post-collisional extension-related geodynamic setting following Late Mesozoic continental collision between the Eurasian plate and Tauride-Anatolide platform, as a result of convergence between the Eurasian and Afro-Arabian plates. (c) 2005 Elsevier Ltd. All rights reserved.