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Automatic identification of posteroanterior cephalometric landmarks using a novel deep learning algorithm: a comparative study with human experts

This study aimed to propose a fully automatic posteroanterior (PA) cephalometric landmark identification model using deep learning algorithms and compare its accuracy and reliability with those of expert human examiners. In total, 1032 PA cephalometric images were used for model training and validat...

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Autores principales: Lee, Hwangyu, Cho, Jung Min, Ryu, Susie, Ryu, Seungmin, Chang, Euijune, Jung, Young-Soo, Kim, Jun-Young
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10509166/
https://www.ncbi.nlm.nih.gov/pubmed/37726392
http://dx.doi.org/10.1038/s41598-023-42870-z
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author Lee, Hwangyu
Cho, Jung Min
Ryu, Susie
Ryu, Seungmin
Chang, Euijune
Jung, Young-Soo
Kim, Jun-Young
author_facet Lee, Hwangyu
Cho, Jung Min
Ryu, Susie
Ryu, Seungmin
Chang, Euijune
Jung, Young-Soo
Kim, Jun-Young
author_sort Lee, Hwangyu
collection PubMed
description This study aimed to propose a fully automatic posteroanterior (PA) cephalometric landmark identification model using deep learning algorithms and compare its accuracy and reliability with those of expert human examiners. In total, 1032 PA cephalometric images were used for model training and validation. Two human expert examiners independently and manually identified 19 landmarks on 82 test set images. Similarly, the constructed artificial intelligence (AI) algorithm automatically identified the landmarks on the images. The mean radial error (MRE) and successful detection rate (SDR) were calculated to evaluate the performance of the model. The performance of the model was comparable with that of the examiners. The MRE of the model was 1.87 ± 1.53 mm, and the SDR was 34.7%, 67.5%, and 91.5% within error ranges of < 1.0, < 2.0, and < 4.0 mm, respectively. The sphenoid points and mastoid processes had the lowest MRE and highest SDR in auto-identification; the condyle points had the highest MRE and lowest SDR. Comparable with human examiners, the fully automatic PA cephalometric landmark identification model showed promising accuracy and reliability and can help clinicians perform cephalometric analysis more efficiently while saving time and effort. Future advancements in AI could further improve the model accuracy and efficiency.
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spelling pubmed-105091662023-09-21 Automatic identification of posteroanterior cephalometric landmarks using a novel deep learning algorithm: a comparative study with human experts Lee, Hwangyu Cho, Jung Min Ryu, Susie Ryu, Seungmin Chang, Euijune Jung, Young-Soo Kim, Jun-Young Sci Rep Article This study aimed to propose a fully automatic posteroanterior (PA) cephalometric landmark identification model using deep learning algorithms and compare its accuracy and reliability with those of expert human examiners. In total, 1032 PA cephalometric images were used for model training and validation. Two human expert examiners independently and manually identified 19 landmarks on 82 test set images. Similarly, the constructed artificial intelligence (AI) algorithm automatically identified the landmarks on the images. The mean radial error (MRE) and successful detection rate (SDR) were calculated to evaluate the performance of the model. The performance of the model was comparable with that of the examiners. The MRE of the model was 1.87 ± 1.53 mm, and the SDR was 34.7%, 67.5%, and 91.5% within error ranges of < 1.0, < 2.0, and < 4.0 mm, respectively. The sphenoid points and mastoid processes had the lowest MRE and highest SDR in auto-identification; the condyle points had the highest MRE and lowest SDR. Comparable with human examiners, the fully automatic PA cephalometric landmark identification model showed promising accuracy and reliability and can help clinicians perform cephalometric analysis more efficiently while saving time and effort. Future advancements in AI could further improve the model accuracy and efficiency. Nature Publishing Group UK 2023-09-19 /pmc/articles/PMC10509166/ /pubmed/37726392 http://dx.doi.org/10.1038/s41598-023-42870-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Lee, Hwangyu
Cho, Jung Min
Ryu, Susie
Ryu, Seungmin
Chang, Euijune
Jung, Young-Soo
Kim, Jun-Young
Automatic identification of posteroanterior cephalometric landmarks using a novel deep learning algorithm: a comparative study with human experts
title Automatic identification of posteroanterior cephalometric landmarks using a novel deep learning algorithm: a comparative study with human experts
title_full Automatic identification of posteroanterior cephalometric landmarks using a novel deep learning algorithm: a comparative study with human experts
title_fullStr Automatic identification of posteroanterior cephalometric landmarks using a novel deep learning algorithm: a comparative study with human experts
title_full_unstemmed Automatic identification of posteroanterior cephalometric landmarks using a novel deep learning algorithm: a comparative study with human experts
title_short Automatic identification of posteroanterior cephalometric landmarks using a novel deep learning algorithm: a comparative study with human experts
title_sort automatic identification of posteroanterior cephalometric landmarks using a novel deep learning algorithm: a comparative study with human experts
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10509166/
https://www.ncbi.nlm.nih.gov/pubmed/37726392
http://dx.doi.org/10.1038/s41598-023-42870-z
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