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    Automatic deep learning detection of overhanging restorations in bitewing radiographs
    (Oxford Univ Press, 2024) Magat, Guldane; Altindag, Ali; Hatipoglu, Fatma Pertek; Hatipoglu, Omer; Bayrakdar, Ibrahim Sevki; Celik, Ozer; Orhan, Kaan
    Objectives This study aimed to assess the effectiveness of deep convolutional neural network (CNN) algorithms for the detecting and segmentation of overhanging dental restorations in bitewing radiographs.Methods A total of 1160 anonymized bitewing radiographs were used to progress the artificial intelligence (AI) system for the detection and segmentation of overhanging restorations. The data were then divided into three groups: 80% for training (930 images, 2399 labels), 10% for validation (115 images, 273 labels), and 10% for testing (115 images, 306 labels). A CNN model known as You Only Look Once (YOLOv5) was trained to detect overhanging restorations in bitewing radiographs. After utilizing the remaining 115 radiographs to evaluate the efficacy of the proposed CNN model, the accuracy, sensitivity, precision, F1 score, and area under the receiver operating characteristic curve (AUC) were computed.Results The model demonstrated a precision of 90.9%, a sensitivity of 85.3%, and an F1 score of 88.0%. Furthermore, the model achieved an AUC of 0.859 on the receiver operating characteristic (ROC) curve. The mean average precision (mAP) at an intersection over a union (IoU) threshold of 0.5 was notably high at 0.87.Conclusions The findings suggest that deep CNN algorithms are highly effective in the detection and diagnosis of overhanging dental restorations in bitewing radiographs. The high levels of precision, sensitivity, and F1 score, along with the significant AUC and mAP values, underscore the potential of these advanced deep learning techniques in revolutionizing dental diagnostic procedures.
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    Comparison of cone-beam computed tomography and digital panoramic radiography for detecting peri-implant alveolar bone changes using trabecular micro-structure analysis
    (Korean Acad Oral & Maxillofacial Surgery, 2022) Magat, Guldane; Oncu, Elif; Ozcan, Sevgi; Orhan, Kaan
    Objectives: We compared changes in fractal dimension (FD) and grayscale value (GSV) of peri-implant alveolar bone on digital panoramic radiograph (DPR) and cone-beam computed tomography (CBCT) immediately after implant surgery and 12 months postoperative. Materials and Methods: In this retrospective study, 16 patients who received posterior mandibular area dental implants with CBCT scans taken about 2 weeks after implantation and one year after implantation were analyzed. A region of interest was selected for each patient. FDs and GSVs were evaluated immediately after implant surgery and at 12-month follow-up to examine the functional loading of the implants. Results: There were no significant differences between DPR and CBCT measurements of FD values (P>0.05). No significant differences were observed between FD values and GSVs calculated after implant surgery and at the 12-month follow-up (F50.05). GSVs were not correlated with FD values (P>0.05). Conclusion: The DPR and reconstructed panoramic CBCT images exhibit similar image quality for the assessment of FD. There were no changes in FD values or GSVs of the pen-implant trabecular bone structure at the 12-month postoperative evaluation of the functional loading of the implant in comparison to values immediately after implantation. GSVs representing bone mass do not align with FD values that predict bone microstnictural parameters. Therefore, GSVs and Ws should be considered different parameters for assessing bone quality.
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    Investigation of Phase Transformation and Fracture Pattern as a Result of Long-Term Chewing Simulation and Static Loading of Reduced-Diameter Zirconia Implants
    (Mdpi, 2024) Atalay Seckiner, Pelin; Gonuldas, Fehmi; Akat, Bora; Buyuksungur, Arda; Orhan, Kaan
    While zirconia implants exhibit osseointegration comparable to that of titanium, concerns arise regarding low-temperature degradation and its potential impact on fracture strength. This study investigated the phase transformation and fracture characteristics of zirconia dental implants after aging through chewing simulation and subsequent static loading. The experimental setup involved 48 one-piece monobloc zirconia implants with diameters of 3.0 mm and 3.7 mm that had straight or angled abutments, with crown restorations, which were divided into six groups based on intraoral regions. The specimens underwent chewing simulation equal to five years of oral service, which was followed by static loading. Statistical analyses were performed for the data obtained from the tests. After dynamic and static loadings, the fractured samples were investigated by Raman spectroscopy to analyze the phase composition and micro-CT to evaluate fracture surfaces and volume changes. According to the results, narrow-diameter zirconia implants have low mechanical durability. The fracture levels, fracture patterns, total porosity, and implant fracture volume values varied according to the implant diameter and phase transformation grade. It was concluded that phase transformation initially guides the propagation of microcracks in zirconia implants, enhancing fracture toughness up to a specific threshold; however, beyond that point, it leads to destructive consequences.