Comparing intra-observer variation and external variations of a fully automated cephalometric analysis with a cascade convolutional neural net

Abstract The quality of cephalometric analysis depends on the accuracy of the delineating landmarks in orthodontic and maxillofacial surgery. Due to the extensive number of landmarks, each analysis costs orthodontists considerable time per patient, leading to fatigue and inter- and intra-observer va...

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Autores principales:	In-Hwan Kim, Young-Gon Kim, Sungchul Kim, Jae-Woo Park, Namkug Kim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/da86601e38c44724a513d0a20be5513c
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spelling	oai:doaj.org-article:da86601e38c44724a513d0a20be5513c2021-12-02T18:03:31ZComparing intra-observer variation and external variations of a fully automated cephalometric analysis with a cascade convolutional neural net10.1038/s41598-021-87261-42045-2322https://doaj.org/article/da86601e38c44724a513d0a20be5513c2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87261-4https://doaj.org/toc/2045-2322Abstract The quality of cephalometric analysis depends on the accuracy of the delineating landmarks in orthodontic and maxillofacial surgery. Due to the extensive number of landmarks, each analysis costs orthodontists considerable time per patient, leading to fatigue and inter- and intra-observer variabilities. Therefore, we proposed a fully automated cephalometry analysis with a cascade convolutional neural net (CNN). One thousand cephalometric x-ray images (2 k × 3 k) pixel were used. The dataset was split into training, validation, and test sets as 8:1:1. The 43 landmarks from each image were identified by an expert orthodontist. To evaluate intra-observer variabilities, 28 images from the dataset were randomly selected and measured again by the same orthodontist. To improve accuracy, a cascade CNN consisting of two steps was used for transfer learning. In the first step, the regions of interest (ROIs) were predicted by RetinaNet. In the second step, U-Net detected the precise landmarks in the ROIs. The average error of ROI detection alone was 1.55 ± 2.17 mm. The model with the cascade CNN showed an average error of 0.79 ± 0.91 mm (paired t-test, p = 0.0015). The orthodontist’s average error of reproducibility was 0.80 ± 0.79 mm. An accurate and fully automated cephalometric analysis was successfully developed and evaluated.In-Hwan KimYoung-Gon KimSungchul KimJae-Woo ParkNamkug KimNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q In-Hwan Kim Young-Gon Kim Sungchul Kim Jae-Woo Park Namkug Kim Comparing intra-observer variation and external variations of a fully automated cephalometric analysis with a cascade convolutional neural net
description	Abstract The quality of cephalometric analysis depends on the accuracy of the delineating landmarks in orthodontic and maxillofacial surgery. Due to the extensive number of landmarks, each analysis costs orthodontists considerable time per patient, leading to fatigue and inter- and intra-observer variabilities. Therefore, we proposed a fully automated cephalometry analysis with a cascade convolutional neural net (CNN). One thousand cephalometric x-ray images (2 k × 3 k) pixel were used. The dataset was split into training, validation, and test sets as 8:1:1. The 43 landmarks from each image were identified by an expert orthodontist. To evaluate intra-observer variabilities, 28 images from the dataset were randomly selected and measured again by the same orthodontist. To improve accuracy, a cascade CNN consisting of two steps was used for transfer learning. In the first step, the regions of interest (ROIs) were predicted by RetinaNet. In the second step, U-Net detected the precise landmarks in the ROIs. The average error of ROI detection alone was 1.55 ± 2.17 mm. The model with the cascade CNN showed an average error of 0.79 ± 0.91 mm (paired t-test, p = 0.0015). The orthodontist’s average error of reproducibility was 0.80 ± 0.79 mm. An accurate and fully automated cephalometric analysis was successfully developed and evaluated.
format	article
author	In-Hwan Kim Young-Gon Kim Sungchul Kim Jae-Woo Park Namkug Kim
author_facet	In-Hwan Kim Young-Gon Kim Sungchul Kim Jae-Woo Park Namkug Kim
author_sort	In-Hwan Kim
title	Comparing intra-observer variation and external variations of a fully automated cephalometric analysis with a cascade convolutional neural net
title_short	Comparing intra-observer variation and external variations of a fully automated cephalometric analysis with a cascade convolutional neural net
title_full	Comparing intra-observer variation and external variations of a fully automated cephalometric analysis with a cascade convolutional neural net
title_fullStr	Comparing intra-observer variation and external variations of a fully automated cephalometric analysis with a cascade convolutional neural net
title_full_unstemmed	Comparing intra-observer variation and external variations of a fully automated cephalometric analysis with a cascade convolutional neural net
title_sort	comparing intra-observer variation and external variations of a fully automated cephalometric analysis with a cascade convolutional neural net
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/da86601e38c44724a513d0a20be5513c
work_keys_str_mv	AT inhwankim comparingintraobservervariationandexternalvariationsofafullyautomatedcephalometricanalysiswithacascadeconvolutionalneuralnet AT younggonkim comparingintraobservervariationandexternalvariationsofafullyautomatedcephalometricanalysiswithacascadeconvolutionalneuralnet AT sungchulkim comparingintraobservervariationandexternalvariationsofafullyautomatedcephalometricanalysiswithacascadeconvolutionalneuralnet AT jaewoopark comparingintraobservervariationandexternalvariationsofafullyautomatedcephalometricanalysiswithacascadeconvolutionalneuralnet AT namkugkim comparingintraobservervariationandexternalvariationsofafullyautomatedcephalometricanalysiswithacascadeconvolutionalneuralnet
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Comparing intra-observer variation and external variations of a fully automated cephalometric analysis with a cascade convolutional neural net

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