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    A deep learning approach to dental restoration classification from bitewing and periapical radiographs
    (Quintessence Publishing Co Inc, 2021) Karataş, Özcan; Cakir, Nazire Nurdan; Ozsariyildiz, Saban Suat; Kis, Hatice Cansu; Demirbuğa, Sezer; Gurgan, Cem Abdulkadir
    Objective: The aim of this study was to examine the success of deep learning-based convolutional neural networks (CNN) in the detection and differentiation of amalgam, composite resin, and metal-ceramic restorations from bitewing and periapical radiographs. Method and materials: Five hundred and fifty bitewing and periapical radiographs were used. Eighty percent of the images were used for training, and 20% were left for testing. Twenty percent of the images allocated for training were then used for validation during learning. The image classification model was based on the application of CNN. The model used Resnet34 architecture, which is pre-trained on the ImageNet dataset. Average sensitivity, receiver operating characteristic (ROC) curve, and area under the curve (AUC) were calculated for performance evaluation of the model Results: The model training loss was 0.13, and the validation loss was 0.63. The independent test group result was 0.67. Amalgam AUC was 0.95, composite AUC was 0.95, and metal-ceramic AUC was 1.00. The average AUC was 0.97. The false positive rate in the validation set was 18, the false negative rate was 18, the true positive rate was 60, and the true negative rate was 138. The true positive rate was 0.82 for amalgam, 0.75 for composite, and 0.73 for metal-ceramic. Conclusion: Deep leaming-based CNNs from periapical and bitewing radiographs appear to be a promising technique for the detection and differentiation of restorations.
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    Effect of systemic atorvastatin on bone regeneration in critical-sized defects in hyperlipidemia: an experimental study
    (Springer Japan Kk, 2023) Ozturk, Kuebra; Kuzu, Turan Emre; Ayrikcil, Semih; Gurgan, Cem Abdulkadir; Onder, Gozde Ozge; Yay, Arzu
    Purpose Hypocholesterolemic medications similar to atorvastatin are efficient in lowering blood lipid levels; however, compared to other medications in the statin family, their impact on bone metabolism is claimed to be insufficient. The impact of atorvastatin on bone regeneration in dental implantology in individuals with hyperlipidemia who received atorvastatin in the clinic is doubtful.Methods In the study, 16 male New Zealand rabbits of 6 months were used. All rabbits were fed a high-cholesterol diet for 8 weeks, and hyperlipidemia was created. It was confirmed that the total cholesterol level in rabbits was above 105 mg/dl. A critical-sized defect was created in the mandible. The defect was closed with xenograft and membrane. Oral 10 mg/kg atorvastatin was started in the experimental group, and no drug was administered in the control group. At 16th week, animals were sacrificed. For histomorphological examination, the new bone area, osteoclast, and osteoblast activities were evaluated.Results While new bone area (45,924 mu m(2), p < 0.001) and AP intensities (105.645 +/- 16.727, p = 0.006) were higher in the atorvastatin group than in the control group, TRAP intensities in the control group (82.192 +/- 5.346, p = 0.021) were higher than that in the atorvastatin group.Conclusions It has been found that high blood lipid levels will adversely affect bone graft healing and the use of systemic atorvastatin contributes to bone healing. Clinicians should pay attention to the selection of surgical materials, considering the importance of questioning drug use in their patients and the risks in cases of non-use.