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Artificial Intelligence (AI)-Driven Molar Angulation Measurements to Predict Third Molar Eruption on Panoramic Radiographs

The purpose of the presented Artificial Intelligence (AI)-tool was to automatically segment the mandibular molars on panoramic radiographs and extract the molar orientations in order to predict the third molars’ eruption potential. In total, 838 panoramic radiographs were used for training (n = 588)...

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Autores principales: Vranckx, Myrthel, Van Gerven, Adriaan, Willems, Holger, Vandemeulebroucke, Arne, Ferreira Leite, André, Politis, Constantinus, Jacobs, Reinhilde
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7277237/
https://www.ncbi.nlm.nih.gov/pubmed/32466156
http://dx.doi.org/10.3390/ijerph17103716
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author Vranckx, Myrthel
Van Gerven, Adriaan
Willems, Holger
Vandemeulebroucke, Arne
Ferreira Leite, André
Politis, Constantinus
Jacobs, Reinhilde
author_facet Vranckx, Myrthel
Van Gerven, Adriaan
Willems, Holger
Vandemeulebroucke, Arne
Ferreira Leite, André
Politis, Constantinus
Jacobs, Reinhilde
author_sort Vranckx, Myrthel
collection PubMed
description The purpose of the presented Artificial Intelligence (AI)-tool was to automatically segment the mandibular molars on panoramic radiographs and extract the molar orientations in order to predict the third molars’ eruption potential. In total, 838 panoramic radiographs were used for training (n = 588) and validation (n = 250) of the network. A fully convolutional neural network with ResNet-101 backbone jointly predicted the molar segmentation maps and an estimate of the orientation lines, which was then iteratively refined by regression on the mesial and distal sides of the segmentation contours. Accuracy was quantified as the fraction of correct angulations (with predefined error intervals) compared to human reference measurements. Performance differences between the network and reference measurements were visually assessed using Bland−Altman plots. The quantitative analysis for automatic molar segmentation resulted in mean IoUs approximating 90%. Mean Hausdorff distances were lowest for first and second molars. The network angulation measurements reached accuracies of 79.7% [−2.5°; 2.5°] and 98.1% [−5°; 5°], combined with a clinically significant reduction in user-time of >53%. In conclusion, this study validated a new and unique AI-driven tool for fast, accurate, and consistent automated measurement of molar angulations on panoramic radiographs. Complementing the dental practitioner with accurate AI-tools will facilitate and optimize dental care and synergistically lead to ever-increasing diagnostic accuracies.
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spelling pubmed-72772372020-06-15 Artificial Intelligence (AI)-Driven Molar Angulation Measurements to Predict Third Molar Eruption on Panoramic Radiographs Vranckx, Myrthel Van Gerven, Adriaan Willems, Holger Vandemeulebroucke, Arne Ferreira Leite, André Politis, Constantinus Jacobs, Reinhilde Int J Environ Res Public Health Article The purpose of the presented Artificial Intelligence (AI)-tool was to automatically segment the mandibular molars on panoramic radiographs and extract the molar orientations in order to predict the third molars’ eruption potential. In total, 838 panoramic radiographs were used for training (n = 588) and validation (n = 250) of the network. A fully convolutional neural network with ResNet-101 backbone jointly predicted the molar segmentation maps and an estimate of the orientation lines, which was then iteratively refined by regression on the mesial and distal sides of the segmentation contours. Accuracy was quantified as the fraction of correct angulations (with predefined error intervals) compared to human reference measurements. Performance differences between the network and reference measurements were visually assessed using Bland−Altman plots. The quantitative analysis for automatic molar segmentation resulted in mean IoUs approximating 90%. Mean Hausdorff distances were lowest for first and second molars. The network angulation measurements reached accuracies of 79.7% [−2.5°; 2.5°] and 98.1% [−5°; 5°], combined with a clinically significant reduction in user-time of >53%. In conclusion, this study validated a new and unique AI-driven tool for fast, accurate, and consistent automated measurement of molar angulations on panoramic radiographs. Complementing the dental practitioner with accurate AI-tools will facilitate and optimize dental care and synergistically lead to ever-increasing diagnostic accuracies. MDPI 2020-05-25 2020-05 /pmc/articles/PMC7277237/ /pubmed/32466156 http://dx.doi.org/10.3390/ijerph17103716 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Vranckx, Myrthel
Van Gerven, Adriaan
Willems, Holger
Vandemeulebroucke, Arne
Ferreira Leite, André
Politis, Constantinus
Jacobs, Reinhilde
Artificial Intelligence (AI)-Driven Molar Angulation Measurements to Predict Third Molar Eruption on Panoramic Radiographs
title Artificial Intelligence (AI)-Driven Molar Angulation Measurements to Predict Third Molar Eruption on Panoramic Radiographs
title_full Artificial Intelligence (AI)-Driven Molar Angulation Measurements to Predict Third Molar Eruption on Panoramic Radiographs
title_fullStr Artificial Intelligence (AI)-Driven Molar Angulation Measurements to Predict Third Molar Eruption on Panoramic Radiographs
title_full_unstemmed Artificial Intelligence (AI)-Driven Molar Angulation Measurements to Predict Third Molar Eruption on Panoramic Radiographs
title_short Artificial Intelligence (AI)-Driven Molar Angulation Measurements to Predict Third Molar Eruption on Panoramic Radiographs
title_sort artificial intelligence (ai)-driven molar angulation measurements to predict third molar eruption on panoramic radiographs
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7277237/
https://www.ncbi.nlm.nih.gov/pubmed/32466156
http://dx.doi.org/10.3390/ijerph17103716
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