Özer Çelik

1.9k total citations
92 papers, 1.1k citations indexed

About

Özer Çelik is a scholar working on Oral Surgery, Biomedical Engineering and Artificial Intelligence. According to data from OpenAlex, Özer Çelik has authored 92 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 56 papers in Oral Surgery, 24 papers in Biomedical Engineering and 15 papers in Artificial Intelligence. Recurrent topics in Özer Çelik's work include Dental Radiography and Imaging (55 papers), Medical Imaging and Analysis (16 papers) and Advanced X-ray and CT Imaging (14 papers). Özer Çelik is often cited by papers focused on Dental Radiography and Imaging (55 papers), Medical Imaging and Analysis (16 papers) and Advanced X-ray and CT Imaging (14 papers). Özer Çelik collaborates with scholars based in Türkiye, United States and Poland. Özer Çelik's co-authors include İbrahim Şevki Bayrakdar, Kaan Orhan, Ahmet Faruk Aslan, Elif Bilgir, Alper Odabaş, Rohan Jagtap, Durmuş Etiz, Sevda Kurt‐Bayrakdar, Yasin Yaşa and Ingrid Różyło‐Kalinowska and has published in prestigious journals such as SHILAP Revista de lepidopterología, BioMed Research International and Journal of Oral and Maxillofacial Surgery.

In The Last Decade

Özer Çelik

67 papers receiving 1.0k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Özer Çelik Türkiye 19 687 256 228 207 202 92 1.1k
Jae Joon Hwang South Korea 17 864 1.3× 318 1.2× 189 0.8× 130 0.6× 127 0.6× 58 1.1k
André Ferreira Leite Brazil 22 998 1.5× 371 1.4× 361 1.6× 136 0.7× 189 0.9× 82 1.9k
Motoki Fukuda Japan 18 1.2k 1.7× 469 1.8× 427 1.9× 342 1.7× 188 0.9× 45 1.6k
Michihito Nozawa Japan 13 569 0.8× 224 0.9× 277 1.2× 190 0.9× 76 0.4× 26 896
Shankeeth Vinayahalingam Netherlands 14 456 0.7× 221 0.9× 179 0.8× 127 0.6× 90 0.4× 55 719
Yun‐Hoa Jung South Korea 18 927 1.3× 219 0.9× 149 0.7× 93 0.4× 95 0.5× 57 1.2k
Kuo Feng Hung Hong Kong 16 733 1.1× 211 0.8× 146 0.6× 71 0.3× 107 0.5× 50 946
Tatsuro Hayashi Japan 15 378 0.6× 236 0.9× 240 1.1× 144 0.7× 35 0.2× 60 803
Praveen Birur India 14 117 0.2× 113 0.4× 189 0.8× 192 0.9× 46 0.2× 57 790
Richard E. Donatelli South Korea 15 538 0.8× 131 0.5× 36 0.2× 41 0.2× 57 0.3× 24 775

Countries citing papers authored by Özer Çelik

Since Specialization
Citations

This map shows the geographic impact of Özer Çelik's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Özer Çelik with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Özer Çelik more than expected).

Fields of papers citing papers by Özer Çelik

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Özer Çelik. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Özer Çelik. The network helps show where Özer Çelik may publish in the future.

Co-authorship network of co-authors of Özer Çelik

This figure shows the co-authorship network connecting the top 25 collaborators of Özer Çelik. A scholar is included among the top collaborators of Özer Çelik based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Özer Çelik. Özer Çelik is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Duman, Şuayip Burak, et al.. (2025). Automatic Segmentation of the Nasolacrimal Canal: Application of the nnU-Net v2 Model in CBCT Imaging. Journal of Clinical Medicine. 14(3). 778–778. 2 indexed citations
2.
Kaya, Yeliz, et al.. (2025). A Novel Machine Learning Model for Predicting Natural Conception Using Non-Laboratory-Based Data. Reproductive Sciences. 32(8). 2644–2653.
3.
4.
Kurt‐Bayrakdar, Sevda, et al.. (2025). Advancing periodontal diagnosis: harnessing advanced artificial intelligence for patterns of periodontal bone loss in cone-beam computed tomography. Dentomaxillofacial Radiology. 54(4). 268–278. 5 indexed citations
5.
Bayrakdar, İbrahim Şevki, et al.. (2025). A Deep Learning Approach for Mandibular Condyle Segmentation on Ultrasonography. Journal of Imaging Informatics in Medicine. 39(1). 286–298.
6.
7.
Çelik, Özer, et al.. (2024). YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition. BMC Medical Imaging. 24(1). 172–172. 12 indexed citations
8.
Kaya, Yeliz, et al.. (2024). Risk Assessment for Preeclampsia in the Preconception Period Based on Maternal Clinical History via Machine Learning Methods. Journal of Clinical Medicine. 14(1). 155–155. 3 indexed citations
9.
Kaya, Çoşkun, et al.. (2024). The prediction of semen quality based on lifestyle behaviours by the machine learning based models. Reproductive Biology and Endocrinology. 22(1). 112–112. 3 indexed citations
10.
Altun, Oğuzhan, Şuayip Burak Duman, İbrahim Şevki Bayrakdar, et al.. (2024). Automatic maxillary sinus segmentation and pathology classification on cone-beam computed tomographic images using deep learning. BMC Oral Health. 24(1). 1208–1208. 4 indexed citations
11.
Çelik, Özer, et al.. (2024). The Detection of Pulp Stones with Automatic Deep Learning in Panoramic Radiographies: An AI Pilot Study. Diagnostics. 14(9). 890–890. 4 indexed citations
12.
Kaya, Yeliz, et al.. (2024). The early prediction of gestational diabetes mellitus by machine learning models. BMC Pregnancy and Childbirth. 24(1). 574–574. 7 indexed citations
13.
Torul, Damla, et al.. (2024). Prediction of extraction difficulty for impacted maxillary third molars with deep learning approach. Journal of Stomatology Oral and Maxillofacial Surgery. 125(4). 101817–101817. 5 indexed citations
14.
Akgül, Nilgün, et al.. (2024). A YOLO-V5 approach for the evaluation of normal fillings and overhanging fillings: an artificial intelligence study. SHILAP Revista de lepidopterología. 38. e098–e098.
15.
Bayrakdar, İbrahim Şevki, et al.. (2024). Evaluation of tooth development stages with deep learning-based artificial intelligence algorithm. BMC Oral Health. 24(1). 1034–1034.
16.
Bayrakdar, İbrahim Şevki, et al.. (2023). Detecting Pulp Stones with Automatic Deep Learning in Bitewing Radiographs: A Pilot Study of Artificial Intelligence. SHILAP Revista de lepidopterología. 50(1). 12–16. 8 indexed citations
17.
Pekiner, Filiz Namdar, et al.. (2023). A Deep Learning Approach to Automatic Tooth Detection and Numbering in Panoramic Radiographs: An Artificial Intelligence Study. Clinical and Experimental Health Sciences. 13(4). 883–888. 1 indexed citations
18.
Bayrakdar, İbrahim Şevki, et al.. (2022). Automatic Detection of Dentigerous Cysts on Panoramic Radiographs: A Deep Learning Study. SHILAP Revista de lepidopterología. 49(1). 1–4. 5 indexed citations
19.
Çelik, Özer, İbrahim Şevki Bayrakdar, Elif Bilgir, et al.. (2022). Detecting the presence of taurodont teeth on panoramic radiographs using a deep learning-based convolutional neural network algorithm. Oral Radiology. 39(1). 207–214. 21 indexed citations
20.
Bayrakdar, İbrahim Şevki, Kaan Orhan, Özer Çelik, et al.. (2022). A U‐Net Approach to Apical Lesion Segmentation on Panoramic Radiographs. BioMed Research International. 2022(1). 7035367–7035367. 47 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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