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Automatized Detection and Categorization of Fissure Sealants from Intraoral Digital Photographs Using Artificial Intelligence

The aim of the present study was to investigate the diagnostic performance of a trained convolutional neural network (CNN) for detecting and categorizing fissure sealants from intraoral photographs using the expert standard as reference. An image set consisting of 2352 digital photographs from perma...

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Autores principales: Schlickenrieder, Anne, Meyer, Ole, Schönewolf, Jule, Engels, Paula, Hickel, Reinhard, Gruhn, Volker, Hesenius, Marc, Kühnisch, Jan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8469974/
https://www.ncbi.nlm.nih.gov/pubmed/34573949
http://dx.doi.org/10.3390/diagnostics11091608
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author Schlickenrieder, Anne
Meyer, Ole
Schönewolf, Jule
Engels, Paula
Hickel, Reinhard
Gruhn, Volker
Hesenius, Marc
Kühnisch, Jan
author_facet Schlickenrieder, Anne
Meyer, Ole
Schönewolf, Jule
Engels, Paula
Hickel, Reinhard
Gruhn, Volker
Hesenius, Marc
Kühnisch, Jan
author_sort Schlickenrieder, Anne
collection PubMed
description The aim of the present study was to investigate the diagnostic performance of a trained convolutional neural network (CNN) for detecting and categorizing fissure sealants from intraoral photographs using the expert standard as reference. An image set consisting of 2352 digital photographs from permanent posterior teeth (461 unsealed tooth surfaces/1891 sealed surfaces) was divided into a training set (n = 1881/364/1517) and a test set (n = 471/97/374). All the images were scored according to the following categories: unsealed molar, intact, sufficient and insufficient sealant. Expert diagnoses served as the reference standard for cyclic training and repeated evaluation of the CNN (ResNeXt-101-32x8d), which was trained by using image augmentation and transfer learning. A statistical analysis was performed, including the calculation of contingency tables and areas under the receiver operating characteristic curve (AUC). The results showed that the CNN accurately detected sealants in 98.7% of all the test images, corresponding to an AUC of 0.996. The diagnostic accuracy and AUC were 89.6% and 0.951, respectively, for intact sealant; 83.2% and 0.888, respectively, for sufficient sealant; 92.4 and 0.942, respectively, for insufficient sealant. On the basis of the documented results, it was concluded that good agreement with the reference standard could be achieved for automatized sealant detection by using artificial intelligence methods. Nevertheless, further research is necessary to improve the model performance.
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spelling pubmed-84699742021-09-27 Automatized Detection and Categorization of Fissure Sealants from Intraoral Digital Photographs Using Artificial Intelligence Schlickenrieder, Anne Meyer, Ole Schönewolf, Jule Engels, Paula Hickel, Reinhard Gruhn, Volker Hesenius, Marc Kühnisch, Jan Diagnostics (Basel) Communication The aim of the present study was to investigate the diagnostic performance of a trained convolutional neural network (CNN) for detecting and categorizing fissure sealants from intraoral photographs using the expert standard as reference. An image set consisting of 2352 digital photographs from permanent posterior teeth (461 unsealed tooth surfaces/1891 sealed surfaces) was divided into a training set (n = 1881/364/1517) and a test set (n = 471/97/374). All the images were scored according to the following categories: unsealed molar, intact, sufficient and insufficient sealant. Expert diagnoses served as the reference standard for cyclic training and repeated evaluation of the CNN (ResNeXt-101-32x8d), which was trained by using image augmentation and transfer learning. A statistical analysis was performed, including the calculation of contingency tables and areas under the receiver operating characteristic curve (AUC). The results showed that the CNN accurately detected sealants in 98.7% of all the test images, corresponding to an AUC of 0.996. The diagnostic accuracy and AUC were 89.6% and 0.951, respectively, for intact sealant; 83.2% and 0.888, respectively, for sufficient sealant; 92.4 and 0.942, respectively, for insufficient sealant. On the basis of the documented results, it was concluded that good agreement with the reference standard could be achieved for automatized sealant detection by using artificial intelligence methods. Nevertheless, further research is necessary to improve the model performance. MDPI 2021-09-03 /pmc/articles/PMC8469974/ /pubmed/34573949 http://dx.doi.org/10.3390/diagnostics11091608 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Communication
Schlickenrieder, Anne
Meyer, Ole
Schönewolf, Jule
Engels, Paula
Hickel, Reinhard
Gruhn, Volker
Hesenius, Marc
Kühnisch, Jan
Automatized Detection and Categorization of Fissure Sealants from Intraoral Digital Photographs Using Artificial Intelligence
title Automatized Detection and Categorization of Fissure Sealants from Intraoral Digital Photographs Using Artificial Intelligence
title_full Automatized Detection and Categorization of Fissure Sealants from Intraoral Digital Photographs Using Artificial Intelligence
title_fullStr Automatized Detection and Categorization of Fissure Sealants from Intraoral Digital Photographs Using Artificial Intelligence
title_full_unstemmed Automatized Detection and Categorization of Fissure Sealants from Intraoral Digital Photographs Using Artificial Intelligence
title_short Automatized Detection and Categorization of Fissure Sealants from Intraoral Digital Photographs Using Artificial Intelligence
title_sort automatized detection and categorization of fissure sealants from intraoral digital photographs using artificial intelligence
topic Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8469974/
https://www.ncbi.nlm.nih.gov/pubmed/34573949
http://dx.doi.org/10.3390/diagnostics11091608
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