Yazar "Abdelkarim, Ahmed Z." seçeneğine göre listele

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    Automated Mesiodens Detection with Deep-Learning-Based System Using Cone-Beam Computed Tomography Images
    (Wiley-Hindawi, 2023) Syed, Ali Zakir; Ozen, Duygu Celik; Abdelkarim, Ahmed Z.; Duman, Suayip Burak; Bayrakdar, Ibrahim Sevki; Duman, Sacide; Celik, Ozer
    The detection of mesiodens supernumerary teeth is crucial for appropriate diagnosis and treatment. The study aimed to develop a convolutional neural network (CNN)-based model to automatically detect mesiodens in cone-beam computed tomography images. A datatest of anonymized 851 axial slices of 106 patients' cone-beam images was used to process the artificial intelligence system for the detection and segmentation of mesiodens. The CNN model achieved high performance in mesiodens segmentation with sensitivity, precision, and F1 scores of 1, 0.9072, and 0.9513, respectively. The area under the curve (AUC) was 0.9147, indicating the model's robustness. The proposed model showed promising potential for the automated detection of mesiodens, providing valuable assistance to dentists in accurate diagnosis.
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    Comparative Evaluation of Temporomandibular Joint Parameters in Unilateral and Bilateral Cleft Lip and Palate Patients Using Cone-Beam CT: Focus on Growing vs. Non-Growing Subjects
    (Mdpi, 2024) Abdelkarim, Ahmed Z.; Almeshari, Ahmed A.; Ozen, Duygu Celik; Khalifa, Ayman R.; Rezallah, Nader N.; Duman, Suayip Burak; Khurana, Sonam
    Background: Morphological differences in the temporomandibular joint (TMJ) are crucial for the treatment of patients with cleft lip and palate (CLP). This study aims to evaluate and compare the TMJ parameters in patients with unilateral and bilateral CLP across growing and non-growing age groups using cone-beam computed tomography (CBCT). Methods: CBCT records from 57 patients (23 males and 34 females) aged 6-50 years with a diagnosed unilateral or bilateral CLP were analyzed. Patients were categorized into four groups: growing unilateral (UGCLP), growing bilateral (BGCLP), non-growing unilateral (UNGCLP), and non-growing bilateral (BNGCLP). Measurements of TMJ parameters, including the mandibular fossa, articular eminence inclination, joint spaces, and roof thickness of the glenoid fossa, were conducted using CBCT images. Results: Significant differences were observed in the anterior joint space (AJS) and the roof of the glenoid fossa (RGF) between growing and non-growing unilateral cleft patients. Additionally, significant discrepancies were found in the articular eminence angle when comparing the cleft and non-cleft sides within the unilateral growing group. No significant differences were observed in TMJ parameters between the right and left sides among bilateral cleft patients. Conclusions: The study highlights distinct TMJ morphological differences between growing and non-growing patients with CLP, emphasizing the importance of age-specific considerations in the treatment planning and growth monitoring of these patients.
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    Comparative Morphometric Study of the Occipital Condyle in Class III and Class I Skeletal Malocclusion Patients
    (Mdpi, 2024) Gumussoy, Ismail; Duman, Suayip Burak; Miloglu, Ozkan; Demirsoy, Mustafa Sami; Dogan, Ayhan; Abdelkarim, Ahmed Z.; Guller, Mustafa Taha
    Objectives: Since the formation of skeletal malocclusions is closely linked to general craniofacial development, it is crucial to understand the anatomy and growth patterns of the skull base. This study aimed to assess the morphometry of the occipital condyle (OC) on CBCT scans of Class III skeletal malocclusion subjects and compare the findings with those of skeletal Class I malocclusion subjects. Methods: A retrospective analysis was performed on CBCT images based on predefined inclusion and exclusion criteria. The sample consisted of 76 CBCT images of 38 skeletal Class III patients and 38 skeletal Class I patients. CBCT scans were used to measure mesiodistal width, sagittal length, coronal height, effective height of OC, and sagittal OC angle. Statistical analyses were conducted with RStudio software. Results: Significant differences were found in sagittal OC angle and sagittal length of OC between the study groups (p < 0.001). In other metrics, such as coronal height of OC, effective OC height, and mesiodistal width of OC between the groups, no significant differences were found. Class III malocclusions exhibited significantly reduced sagittal OC angle and sagittal length of OC compared to Class I malocclusions. The left side showed a significantly larger sagittal OC angle than the right side (p = 0.002). Conclusions: This preliminary study identified reduced sagittal angle and sagittal length of OC in patients with Class III skeletal malocclusion. Clinicians should recognize potential differences in OC morphometry in patients with skeletal malocclusions. Future studies involving larger populations are recommended to further investigate the relationship between skeletal malocclusions and posterior cranial base structures, including the OC.
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    Detecting white spot lesions on post-orthodontic oral photographs using deep learning based on the YOLOv5x algorithm: a pilot study
    (Bmc, 2024) Ozsunkar, Pelin Senem; Ozen, Duygu CelIk; Abdelkarim, Ahmed Z.; Duman, Sacide; Ugurlu, Mehmet; Demir, Mehmet Ridvan; Kuleli, Batuhan
    Background Deep learning model trained on a large image dataset, can be used to detect and discriminate targets with similar but not identical appearances. The aim of this study is to evaluate the post-training performance of the CNN-based YOLOv5x algorithm in the detection of white spot lesions in post-orthodontic oral photographs using the limited data available and to make a preliminary study for fully automated models that can be clinically integrated in the future.Methods A total of 435 images in JPG format were uploaded into the CranioCatch labeling software and labeled white spot lesions. The labeled images were resized to 640 x 320 while maintaining their aspect ratio before model training. The labeled images were randomly divided into three groups (Training:349 images (1589 labels), Validation:43 images (181 labels), Test:43 images (215 labels)). YOLOv5x algorithm was used to perform deep learning. The segmentation performance of the tested model was visualized and analyzed using ROC analysis and a confusion matrix. True Positive (TP), False Positive (FP), and False Negative (FN) values were determined.Results Among the test group images, there were 133 TPs, 36 FPs, and 82 FNs. The model's performance metrics include precision, recall, and F1 score values of detecting white spot lesions were 0.786, 0.618, and 0.692. The AUC value obtained from the ROC analysis was 0.712. The mAP value obtained from the Precision-Recall curve graph was 0.425.Conclusions The model's accuracy and sensitivity in detecting white spot lesions remained lower than expected for practical application, but is a promising and acceptable detection rate compared to previous study. The current study provides a preliminary insight to further improved by increasing the dataset for training, and applying modifications to the deep learning algorithm.Clinical revelance Deep learning systems can help clinicians to distinguish white spot lesions that may be missed during visual inspection.
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    MorphMaskFormer: a transformer-based deep segmentation model for multi-class Demirjian stage estimation from panoramic radiographs
    (Elsevier Inc., 2026) Kıranşal, Melike; Özçelik, Salih Talha Alperen; Aydan, Tuba; Üzen, Hüseyin; Fırat, Hüseyin; Şengür, Abdulkadir; Abdelkarim, Ahmed Z.
    Objectives This study aims to develop an advanced deep learning model that automatically determines third-molar developmental stages in panoramic radiographs using the Demirjian classification, improving the accuracy and objectivity of dental age estimation for forensic and clinical applications. Study Design A total of 888 panoramic radiographs from individuals aged 7 to 30 were annotated by 2 experts based on Demirjian’s A–H staging system. The proposed model, MorphMaskFormer , is built upon the classical UNet architecture, incorporating a lightweight transformer attention module inspired by Mask2Former. The model performs both binary (tooth/background) and multi-class (A–H stages) segmentation. Its performance was evaluated using IoU, Dice coefficient, Precision, Recall, and inference time, and compared against UNet, ResUNet, DeepLabV3+, PSPNet, and SegNet. Results MorphMaskFormer outperformed all baseline models, achieving a Dice score of 0.9461, IoU of 0.8985, and the fastest inference time at 78.59 ms. In multi-class segmentation, it showed high accuracy for stages A, D, and H, with an overall component accuracy of 72.41%. Conclusions MorphMaskFormer enables precise pixel-level segmentation of dental developmental stages, reducing inter-observer variability and shortening evaluation time. Its high accuracy and efficiency make it a scalable tool that enhances diagnostic confidence and supports critical clinical and forensic age-estimation decisions. © 2026 Elsevier Inc.
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    Nasopharynx evaluation in children of unilateral cleft palate patients and normal with cone beam computed tomography
    (Sage Publications Ltd, 2023) Temiz, Mustafa; Duman, Suayip Burak; Abdelkarim, Ahmed Z.; Bayrakdar, Ibrahim Sevki; Syed, Ali Z.; Eser, Gozde; Celik Ozen, Duygu
    Objective:This study aimed to examine the morphological characteristics of the nasopharynx in unilateral Cleft lip/palate (CL/P) children and non-cleft children using cone beam computed tomography (CBCT). Methods:A retrospective study consisted of 54 patients, of which 27 patients were unilateral CL/P, remaining 27 patients have no CL/P. Eustachian tubes orifice (ET), Rosenmuller fossa (RF) depth, presence of pharyngeal bursa (PB), the distance of posterior nasal spine (PNS)-pharynx posterior wall were quantitatively evaluated. Results:The main effect of the CL/P groups was found to be effective on RF depth-right (p < 0.001) and RF depth-left (p < 0.001). The interaction effect of gender and CL/P groups was not influential on measurements. The cleft-side main effect was found to be effective on RF depth-left (p < 0.001) and RF depth-right (p = 0002). There was no statistically significant relationship between CL/P groups and the presence of bursa pharyngea. Conclusions:Because it is the most common site of nasopharyngeal carcinoma (NPC), the anatomy of the nasopharynx should be well known in the early diagnosis of NPC.