Yazar "Bayrakdar, İbrahim Şevki" için Diş Hekimliği Fakültesi 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 ... -
Evaluation of sphenoid sinus variations by using cone beam computed tomography in patients with cleft lip/palate
Gümrükçü, Zeynep; Bilgir, Elif; Bayrak, Seval; Bayrakdar, İbrahim Şevki (Springer, 2020)Objective the purpose of this study is to evaluate the sphenoid sinus variations in patients with cleft lip/palate. Methods Sixty three (63) patient's cone beam computed tomography (CBCT) images, taken from the head and ... -
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 ... -
Fractal properties of the trabecular pattern of the mandible in chronic renal failure
Gümüşsoy, İsmail; Miloğlu, Özkan; Çankaya, Erdem; Bayrakdar, İbrahim Şevki (British Inst Radiology, 2016)Objectives: the purpose of this study was to evaluate fractal analysis as a tool to quantitatively determine the mandibular trabecular bone changes in patients with chronic renal failure (CRF). Methods: in the present ... -
Morphometric analysis of sella Turcica using cone beam computed tomography
Yasa, Yasin; Ocak, Ali; Bayrakdar, İbrahim Şevki; Duman, Şuayip Burak; Gümüşsoy, İsmail (Lippincott Williams & Wilkins, 2017)Objective: the purpose of this study was to assess morphological shape and morphometric analysis of the sella turcica using cone beam computed tomography (CBCT) in different planes of section (coronal and sagittal). Materials ... -
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 ...