Yazar "Çelik, Özer" için listeleme
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Correction: YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition
Beşer, Büşra; Reis, Tuğba; Berber, Merve Nur; Topaloğlu, Edanur; Güngör, Esra; Kılıç, Münevver Çoruh; Duman, Sacide; Çelik, Özer; Kuran, Alican; Bayrakdar, İbrahim Şevki (BMC, 2024).... -
Detection of periodontal bone loss patterns and furcation defects from panoramic radiographs using deep learning algorithm: a retrospective study
Kurt-Bayrakdar, Sevda; Bayrakdar, İbrahim Şevki; Yavuz, Muhammet Burak; Sali, Nichal; Çelik, Özer; Köse, Oğuz; Uzun Saylan, Bilge Cansu; Kuleli, Batuhan; Jagtap, Rohan; Orhan, Kaan (Springer Link, 2024)Background: This retrospective study aimed to develop a deep learning algorithm for the interpretation of panoramic radiographs and to examine the performance of this algorithm in the detection of periodontal bone losses ... -
Detection of tooth numbering, frenulum attachment, gingival overgrowth, and gingival inflammation signs on dental photographs using convolutional neural network algorithms: a retrospective study
Kurt Bayrakdar, Sevda; Uğurlu, Mehmet; Yavuz, Muhammet Burak; Sali, Nichal; Bayrakdar, İbrahim Şevki; Çelik, Özer; Köse, Oğuz; Beklen, Arzu; Saylan, Bilge Cansu Uzun; Jagtap, Rohan; Orhan, Kaan (Quintessence Publishing, 2023)Objectives: This study aimed to develop an artificial intelligence (Al) model that can determine automatic tooth numbering, frenulum attachments, gingival overgrowth areas, and gingival inflammation signs on intraoral ... -
Does the FARNet neural network algorithm accurately identify Posteroanterior cephalometric landmarks?
Gonca, Merve; Bayrakdar, İbrahim Şevki; Çelik, Özer (BioMed Central Ltd, 2024)Background: We explored whether the feature aggregation and refinement network (FARNet) algorithm accurately identified posteroanterior (PA) cephalometric landmarks. Methods: We identified 47 landmarks on 1,431 PA cephalograms ... -
Evaluation of tooth development stages with deep learning-based artificial intelligence algorithm
Kurt, Ayça; Günaçar, Dilara Nil; Şılbır, Fatma Yanık; Yeşil, Zeynep; Bayrakdar, İbrahim Şevki; Çelik, Özer; Bilgir, Elif; Orhan, Kaan (Springer, 2024)Background: This study aims to evaluate the performance of a deep learning system for the evaluation of tooth development stages on images obtained from panoramic radiographs from child patients. Methods: The study collected ... -
YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition
Beşer, Büşra; Reis, Tuğba; Berber, Merve Nur; Topaloğlu, Edanur; Güngör, Esra; Kılıç, Münevver Çoruh; Duman, Sacide; Çelik, Özer; Kuran, Alican; Bayrakdar, İbrahim Şevki (Springer, 2024)Objectives: In the interpretation of panoramic radiographs (PRs), the identification and numbering of teeth is an important part of the correct diagnosis. This study evaluates the effectiveness of YOLO-v5 in the automatic ...
