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Communication

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

1
Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians University Munich, 80336 Munich, Germany
2
Institute for Software Engineering, University of Duisburg-Essen, 45147 Essen, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Jae-Hong Lee
Diagnostics 2021, 11(9), 1608; https://doi.org/10.3390/diagnostics11091608
Received: 2 August 2021 / Revised: 31 August 2021 / Accepted: 1 September 2021 / Published: 3 September 2021
(This article belongs to the Special Issue Artificial Intelligence in Oral Health)
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. View Full-Text
Keywords: pit and fissure sealants; caries assessment; visual examination; clinical evaluation; artificial intelligence; convolutional neural networks; deep learning; transfer learning pit and fissure sealants; caries assessment; visual examination; clinical evaluation; artificial intelligence; convolutional neural networks; deep learning; transfer learning
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MDPI and ACS Style

Schlickenrieder, A.; Meyer, O.; Schönewolf, J.; Engels, P.; Hickel, R.; Gruhn, V.; Hesenius, M.; Kühnisch, J. Automatized Detection and Categorization of Fissure Sealants from Intraoral Digital Photographs Using Artificial Intelligence. Diagnostics 2021, 11, 1608. https://doi.org/10.3390/diagnostics11091608

AMA Style

Schlickenrieder A, Meyer O, Schönewolf J, Engels P, Hickel R, Gruhn V, Hesenius M, Kühnisch J. Automatized Detection and Categorization of Fissure Sealants from Intraoral Digital Photographs Using Artificial Intelligence. Diagnostics. 2021; 11(9):1608. https://doi.org/10.3390/diagnostics11091608

Chicago/Turabian Style

Schlickenrieder, Anne, Ole Meyer, Jule Schönewolf, Paula Engels, Reinhard Hickel, Volker Gruhn, Marc Hesenius, and Jan Kühnisch. 2021. "Automatized Detection and Categorization of Fissure Sealants from Intraoral Digital Photographs Using Artificial Intelligence" Diagnostics 11, no. 9: 1608. https://doi.org/10.3390/diagnostics11091608

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