Automatic detection of the mental foramen for estimating mandibular cortical width in dental panoramic radiographs: the seventh survey of the Tromsø Study (Tromsø7) in 2015–2016
Objective To apply deep learning to a data set of dental panoramic radiographs to detect the mental foramen for automatic assessment of the mandibular cortical width. Methods Data from the seventh survey of the Tromsø Study (Tromsø7) were used. The data set contained 5197 randomly chosen dental pano...
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ftdoajarticles:oai:doaj.org/article:5ba9b2e3861d47188714a2d71191fd2a 2023-05-15T18:34:25+02:00 Automatic detection of the mental foramen for estimating mandibular cortical width in dental panoramic radiographs: the seventh survey of the Tromsø Study (Tromsø7) in 2015–2016 Isak Paasche Edvardsen Anna Teterina Thomas Johansen Jonas Nordhaug Myhre Fred Godtliebsen Napat Limchaichana Bolstad 2022-11-01T00:00:00Z https://doi.org/10.1177/03000605221135147 https://doaj.org/article/5ba9b2e3861d47188714a2d71191fd2a EN eng SAGE Publishing https://doi.org/10.1177/03000605221135147 https://doaj.org/toc/1473-2300 1473-2300 doi:10.1177/03000605221135147 https://doaj.org/article/5ba9b2e3861d47188714a2d71191fd2a Journal of International Medical Research, Vol 50 (2022) Medicine (General) R5-920 article 2022 ftdoajarticles https://doi.org/10.1177/03000605221135147 2022-12-30T19:38:00Z Objective To apply deep learning to a data set of dental panoramic radiographs to detect the mental foramen for automatic assessment of the mandibular cortical width. Methods Data from the seventh survey of the Tromsø Study (Tromsø7) were used. The data set contained 5197 randomly chosen dental panoramic radiographs. Four pretrained object detectors were tested. We randomly chose 80% of the data for training and 20% for testing. Models were trained using GeForce RTX 2080 Ti with 11 GB GPU memory (NVIDIA Corporation, Santa Clara, CA, USA). Python programming language version 3.7 was used for analysis. Results The EfficientDet-D0 model showed the highest average precision of 0.30. When the threshold to regard a prediction as correct (intersection over union) was set to 0.5, the average precision was 0.79. The RetinaNet model achieved the lowest average precision of 0.23, and the precision was 0.64 when the intersection over union was set to 0.5. The procedure to estimate mandibular cortical width showed acceptable results. Of 100 random images, the algorithm produced an output 93 times, 20 of which were not visually satisfactory. Conclusions EfficientDet-D0 effectively detected the mental foramen. Methods for estimating bone quality are important in radiology and require further development. Article in Journal/Newspaper Tromsø Directory of Open Access Journals: DOAJ Articles Tromsø Journal of International Medical Research 50 11 030006052211351 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
Medicine (General) R5-920 |
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Medicine (General) R5-920 Isak Paasche Edvardsen Anna Teterina Thomas Johansen Jonas Nordhaug Myhre Fred Godtliebsen Napat Limchaichana Bolstad Automatic detection of the mental foramen for estimating mandibular cortical width in dental panoramic radiographs: the seventh survey of the Tromsø Study (Tromsø7) in 2015–2016 |
topic_facet |
Medicine (General) R5-920 |
description |
Objective To apply deep learning to a data set of dental panoramic radiographs to detect the mental foramen for automatic assessment of the mandibular cortical width. Methods Data from the seventh survey of the Tromsø Study (Tromsø7) were used. The data set contained 5197 randomly chosen dental panoramic radiographs. Four pretrained object detectors were tested. We randomly chose 80% of the data for training and 20% for testing. Models were trained using GeForce RTX 2080 Ti with 11 GB GPU memory (NVIDIA Corporation, Santa Clara, CA, USA). Python programming language version 3.7 was used for analysis. Results The EfficientDet-D0 model showed the highest average precision of 0.30. When the threshold to regard a prediction as correct (intersection over union) was set to 0.5, the average precision was 0.79. The RetinaNet model achieved the lowest average precision of 0.23, and the precision was 0.64 when the intersection over union was set to 0.5. The procedure to estimate mandibular cortical width showed acceptable results. Of 100 random images, the algorithm produced an output 93 times, 20 of which were not visually satisfactory. Conclusions EfficientDet-D0 effectively detected the mental foramen. Methods for estimating bone quality are important in radiology and require further development. |
format |
Article in Journal/Newspaper |
author |
Isak Paasche Edvardsen Anna Teterina Thomas Johansen Jonas Nordhaug Myhre Fred Godtliebsen Napat Limchaichana Bolstad |
author_facet |
Isak Paasche Edvardsen Anna Teterina Thomas Johansen Jonas Nordhaug Myhre Fred Godtliebsen Napat Limchaichana Bolstad |
author_sort |
Isak Paasche Edvardsen |
title |
Automatic detection of the mental foramen for estimating mandibular cortical width in dental panoramic radiographs: the seventh survey of the Tromsø Study (Tromsø7) in 2015–2016 |
title_short |
Automatic detection of the mental foramen for estimating mandibular cortical width in dental panoramic radiographs: the seventh survey of the Tromsø Study (Tromsø7) in 2015–2016 |
title_full |
Automatic detection of the mental foramen for estimating mandibular cortical width in dental panoramic radiographs: the seventh survey of the Tromsø Study (Tromsø7) in 2015–2016 |
title_fullStr |
Automatic detection of the mental foramen for estimating mandibular cortical width in dental panoramic radiographs: the seventh survey of the Tromsø Study (Tromsø7) in 2015–2016 |
title_full_unstemmed |
Automatic detection of the mental foramen for estimating mandibular cortical width in dental panoramic radiographs: the seventh survey of the Tromsø Study (Tromsø7) in 2015–2016 |
title_sort |
automatic detection of the mental foramen for estimating mandibular cortical width in dental panoramic radiographs: the seventh survey of the tromsø study (tromsø7) in 2015–2016 |
publisher |
SAGE Publishing |
publishDate |
2022 |
url |
https://doi.org/10.1177/03000605221135147 https://doaj.org/article/5ba9b2e3861d47188714a2d71191fd2a |
geographic |
Tromsø |
geographic_facet |
Tromsø |
genre |
Tromsø |
genre_facet |
Tromsø |
op_source |
Journal of International Medical Research, Vol 50 (2022) |
op_relation |
https://doi.org/10.1177/03000605221135147 https://doaj.org/toc/1473-2300 1473-2300 doi:10.1177/03000605221135147 https://doaj.org/article/5ba9b2e3861d47188714a2d71191fd2a |
op_doi |
https://doi.org/10.1177/03000605221135147 |
container_title |
Journal of International Medical Research |
container_volume |
50 |
container_issue |
11 |
container_start_page |
030006052211351 |
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1766219140014538752 |