Publication: Artificial intelligence technologies in dentistry
| dc.contributor.author | Albayrak, Berkman | |
| dc.contributor.author | Özdemir, Gökhan | |
| dc.contributor.author | Us, Yeşim Olçer | |
| dc.contributor.author | Yüzbaşıoğlu, Emir | |
| dc.contributor.institution | Albayrak, Berkman, Department of Prosthodontics, Bahçeşehir Üniversitesi, Istanbul, Turkey | |
| dc.contributor.institution | Özdemir, Gökhan, Department of Prosthodontics, Bahçeşehir Üniversitesi, Istanbul, Turkey | |
| dc.contributor.institution | Us, Yeşim Olçer, Department of Prosthodontics, Bahçeşehir Üniversitesi, Istanbul, Turkey | |
| dc.contributor.institution | Yüzbaşıoğlu, Emir, Department of Prosthodontics, Bahçeşehir Üniversitesi, Istanbul, Turkey, Department of Dentistry, BAU International University, Batumi, Georgia | |
| dc.date.accessioned | 2025-10-05T15:34:12Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | One of the most important actors in the digitization process of our age has been the applications of artificial intelligence (AI). While the weak and strong AI sub-concepts and the different AI models within them are being utilized in many fields such as education, industry and medicine today, the interest of the dentistry field, which has started its integration into the digital world with CAD/CAM technology, in AI is increasing day by day. In different branches of dentistry, AI provides services to clinicians and researchers in many fields such as disease diagnosis, evaluation of the occurrence or recurrence of diseases such as oral cancer, and prediction of success in surgical and prosthetic treatments. In this article, studies in which AI models such as machine learning, convolutional neural networks have found research and usage areas on the basis of different branches of dentistry are reviewed. © 2021 Elsevier B.V., All rights reserved. | |
| dc.identifier.doi | 10.52142/OMUJECM.38.SI.DENT.18 | |
| dc.identifier.endpage | 194 | |
| dc.identifier.issn | 13094483 | |
| dc.identifier.issn | 13095129 | |
| dc.identifier.scopus | 2-s2.0-85106462261 | |
| dc.identifier.startpage | 188 | |
| dc.identifier.uri | https://doi.org/10.52142/OMUJECM.38.SI.DENT.18 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14719/9670 | |
| dc.identifier.volume | 38 | |
| dc.language.iso | en | |
| dc.publisher | Ondokuz Mayis Universitesi | |
| dc.relation.oastatus | All Open Access | |
| dc.relation.oastatus | Bronze Open Access | |
| dc.relation.oastatus | Green Accepted Open Access | |
| dc.relation.oastatus | Green Open Access | |
| dc.relation.source | Journal of Experimental and Clinical Medicine (Turkey) | |
| dc.subject.authorkeywords | Artificial Intelligence | |
| dc.subject.authorkeywords | Deep Learning | |
| dc.subject.authorkeywords | Dentistry | |
| dc.subject.authorkeywords | Neural Networks | |
| dc.subject.authorkeywords | Prediction | |
| dc.subject.authorkeywords | Artificial Intelligence | |
| dc.subject.authorkeywords | Computer Aided Design/computer Aided Manufacturing | |
| dc.subject.authorkeywords | Computer Assisted Tomography | |
| dc.subject.authorkeywords | Convolutional Neural Network | |
| dc.subject.authorkeywords | Deep Learning | |
| dc.subject.authorkeywords | Dentistry | |
| dc.subject.authorkeywords | Endodontics | |
| dc.subject.authorkeywords | Human | |
| dc.subject.authorkeywords | Image Reconstruction | |
| dc.subject.authorkeywords | Jaw Disease | |
| dc.subject.authorkeywords | Maxillofacial Surgery | |
| dc.subject.authorkeywords | Oral Surgery | |
| dc.subject.authorkeywords | Orthodontics | |
| dc.subject.authorkeywords | Pediatric Dentistry | |
| dc.subject.authorkeywords | Periodontics | |
| dc.subject.authorkeywords | Prosthodontics | |
| dc.subject.authorkeywords | Restorative Dentistry | |
| dc.subject.authorkeywords | Review | |
| dc.subject.authorkeywords | Tooth Radiography | |
| dc.subject.indexkeywords | artificial intelligence | |
| dc.subject.indexkeywords | computer aided design/computer aided manufacturing | |
| dc.subject.indexkeywords | computer assisted tomography | |
| dc.subject.indexkeywords | convolutional neural network | |
| dc.subject.indexkeywords | deep learning | |
| dc.subject.indexkeywords | dentistry | |
| dc.subject.indexkeywords | endodontics | |
| dc.subject.indexkeywords | human | |
| dc.subject.indexkeywords | image reconstruction | |
| dc.subject.indexkeywords | jaw disease | |
| dc.subject.indexkeywords | maxillofacial surgery | |
| dc.subject.indexkeywords | oral surgery | |
| dc.subject.indexkeywords | orthodontics | |
| dc.subject.indexkeywords | pediatric dentistry | |
| dc.subject.indexkeywords | periodontics | |
| dc.subject.indexkeywords | prosthodontics | |
| dc.subject.indexkeywords | restorative dentistry | |
| dc.subject.indexkeywords | Review | |
| dc.subject.indexkeywords | tooth radiography | |
| dc.title | Artificial intelligence technologies in dentistry | |
| dc.type | Review | |
| dcterms.references | Okan Akçam, M., Fuzzy modelling for selecting headgear types, European Journal of Orthodontics, 24, 1, pp. 99-106, (2002), Alabi, Rasheed Omobolaji, Machine learning application for prediction of locoregional recurrences in early oral tongue cancer: a Web-based prognostic tool, Virchows Archiv, 475, 4, pp. 489-497, (2019), Aliaga, Ignacio J., Modelling the longevity of dental restorations by means of a CBR system, BioMed Research International, 2015, (2015), Baliga, M. S., Artificial intelligence-The next frontier in pediatric dentistry, Journal of Indian Society of Pedodontics and Preventive Dentistry, 37, 4, (2019), Baş, Burcu, Use of artificial neural network in differentiation of subgroups of temporomandibular internal derangements: A preliminary study, Journal of Oral and Maxillofacial Surgery, 70, 1, pp. 51-59, (2012), Borza, Diana Laura, Automatic skin tone extraction for visagism applications, 4, pp. 466-473, (2018), Boulétreau, Pierre, Artificial Intelligence: Applications in orthognathic surgery, Journal of Stomatology, Oral and Maxillofacial Surgery, 120, 4, pp. 347-354, (2019), Burt, Jeremy R., Deep learning beyond cats and dogs: Recent advances in diagnosing breast cancer with deep neural networks, British Journal of Radiology, 91, 1089, (2018), Chen, Qingxiao, An ontology-driven, case-based clinical decision support model for removable partial denture design, Scientific Reports, 6, (2016), Cheng, Cheng, Prediction of facial deformation after complete denture prosthesis using BP neural network, Computers in Biology and Medicine, 66, pp. 103-112, (2015) | |
| dspace.entity.type | Publication | |
| local.indexed.at | Scopus | |
| person.identifier.scopus-author-id | 57219149269 | |
| person.identifier.scopus-author-id | 57223938307 | |
| person.identifier.scopus-author-id | 57195279912 | |
| person.identifier.scopus-author-id | 15063768900 |
