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An artifıcial ıntelligence approach to automatic tooth detection and numbering in panoramic radiographs

BACKGROUND: Panoramic radiography is an imaging method for displaying maxillary and mandibular teeth together with their supporting structures. Panoramic radiography is frequently used in dental imaging due to its relatively low radiation dose, short imaging time, and low burden to the patient. We v...

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Autores principales: Bilgir, Elif, Bayrakdar, İbrahim Şevki, Çelik, Özer, Orhan, Kaan, Akkoca, Fatma, Sağlam, Hande, Odabaş, Alper, Aslan, Ahmet Faruk, Ozcetin, Cemre, Kıllı, Musa, Rozylo-Kalinowska, Ingrid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8361658/
https://www.ncbi.nlm.nih.gov/pubmed/34388975
http://dx.doi.org/10.1186/s12880-021-00656-7
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author Bilgir, Elif
Bayrakdar, İbrahim Şevki
Çelik, Özer
Orhan, Kaan
Akkoca, Fatma
Sağlam, Hande
Odabaş, Alper
Aslan, Ahmet Faruk
Ozcetin, Cemre
Kıllı, Musa
Rozylo-Kalinowska, Ingrid
author_facet Bilgir, Elif
Bayrakdar, İbrahim Şevki
Çelik, Özer
Orhan, Kaan
Akkoca, Fatma
Sağlam, Hande
Odabaş, Alper
Aslan, Ahmet Faruk
Ozcetin, Cemre
Kıllı, Musa
Rozylo-Kalinowska, Ingrid
author_sort Bilgir, Elif
collection PubMed
description BACKGROUND: Panoramic radiography is an imaging method for displaying maxillary and mandibular teeth together with their supporting structures. Panoramic radiography is frequently used in dental imaging due to its relatively low radiation dose, short imaging time, and low burden to the patient. We verified the diagnostic performance of an artificial intelligence (AI) system based on a deep convolutional neural network method to detect and number teeth on panoramic radiographs. METHODS: The data set included 2482 anonymized panoramic radiographs from adults from the archive of Eskisehir Osmangazi University, Faculty of Dentistry, Department of Oral and Maxillofacial Radiology. A Faster R-CNN Inception v2 model was used to develop an AI algorithm (CranioCatch, Eskisehir, Turkey) to automatically detect and number teeth on panoramic radiographs. Human observation and AI methods were compared on a test data set consisting of 249 panoramic radiographs. True positive, false positive, and false negative rates were calculated for each quadrant of the jaws. The sensitivity, precision, and F-measure values were estimated using a confusion matrix. RESULTS: The total numbers of true positive, false positive, and false negative results were 6940, 250, and 320 for all quadrants, respectively. Consequently, the estimated sensitivity, precision, and F-measure were 0.9559, 0.9652, and 0.9606, respectively. CONCLUSIONS: The deep convolutional neural network system was successful in detecting and numbering teeth. Clinicians can use AI systems to detect and number teeth on panoramic radiographs, which may eventually replace evaluation by human observers and support decision making.
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spelling pubmed-83616582021-08-17 An artifıcial ıntelligence approach to automatic tooth detection and numbering in panoramic radiographs Bilgir, Elif Bayrakdar, İbrahim Şevki Çelik, Özer Orhan, Kaan Akkoca, Fatma Sağlam, Hande Odabaş, Alper Aslan, Ahmet Faruk Ozcetin, Cemre Kıllı, Musa Rozylo-Kalinowska, Ingrid BMC Med Imaging Research BACKGROUND: Panoramic radiography is an imaging method for displaying maxillary and mandibular teeth together with their supporting structures. Panoramic radiography is frequently used in dental imaging due to its relatively low radiation dose, short imaging time, and low burden to the patient. We verified the diagnostic performance of an artificial intelligence (AI) system based on a deep convolutional neural network method to detect and number teeth on panoramic radiographs. METHODS: The data set included 2482 anonymized panoramic radiographs from adults from the archive of Eskisehir Osmangazi University, Faculty of Dentistry, Department of Oral and Maxillofacial Radiology. A Faster R-CNN Inception v2 model was used to develop an AI algorithm (CranioCatch, Eskisehir, Turkey) to automatically detect and number teeth on panoramic radiographs. Human observation and AI methods were compared on a test data set consisting of 249 panoramic radiographs. True positive, false positive, and false negative rates were calculated for each quadrant of the jaws. The sensitivity, precision, and F-measure values were estimated using a confusion matrix. RESULTS: The total numbers of true positive, false positive, and false negative results were 6940, 250, and 320 for all quadrants, respectively. Consequently, the estimated sensitivity, precision, and F-measure were 0.9559, 0.9652, and 0.9606, respectively. CONCLUSIONS: The deep convolutional neural network system was successful in detecting and numbering teeth. Clinicians can use AI systems to detect and number teeth on panoramic radiographs, which may eventually replace evaluation by human observers and support decision making. BioMed Central 2021-08-13 /pmc/articles/PMC8361658/ /pubmed/34388975 http://dx.doi.org/10.1186/s12880-021-00656-7 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Bilgir, Elif
Bayrakdar, İbrahim Şevki
Çelik, Özer
Orhan, Kaan
Akkoca, Fatma
Sağlam, Hande
Odabaş, Alper
Aslan, Ahmet Faruk
Ozcetin, Cemre
Kıllı, Musa
Rozylo-Kalinowska, Ingrid
An artifıcial ıntelligence approach to automatic tooth detection and numbering in panoramic radiographs
title An artifıcial ıntelligence approach to automatic tooth detection and numbering in panoramic radiographs
title_full An artifıcial ıntelligence approach to automatic tooth detection and numbering in panoramic radiographs
title_fullStr An artifıcial ıntelligence approach to automatic tooth detection and numbering in panoramic radiographs
title_full_unstemmed An artifıcial ıntelligence approach to automatic tooth detection and numbering in panoramic radiographs
title_short An artifıcial ıntelligence approach to automatic tooth detection and numbering in panoramic radiographs
title_sort artifıcial ıntelligence approach to automatic tooth detection and numbering in panoramic radiographs
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8361658/
https://www.ncbi.nlm.nih.gov/pubmed/34388975
http://dx.doi.org/10.1186/s12880-021-00656-7
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