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Evaluation of fully automated cephalometric measurements obtained from web-based artificial intelligence driven platform
BACKGROUND: Artificial Intelligence has created a huge impact in different areas of dentistry. Automated cephalometric analysis is one of the major applications of artificial intelligence in the field of orthodontics. Various automated cephalometric software have been developed which utilizes artifi...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9020017/ https://www.ncbi.nlm.nih.gov/pubmed/35440037 http://dx.doi.org/10.1186/s12903-022-02170-w |
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author | Mahto, Ravi Kumar Kafle, Dashrath Giri, Abhishek Luintel, Sanjeev Karki, Arjun |
author_facet | Mahto, Ravi Kumar Kafle, Dashrath Giri, Abhishek Luintel, Sanjeev Karki, Arjun |
author_sort | Mahto, Ravi Kumar |
collection | PubMed |
description | BACKGROUND: Artificial Intelligence has created a huge impact in different areas of dentistry. Automated cephalometric analysis is one of the major applications of artificial intelligence in the field of orthodontics. Various automated cephalometric software have been developed which utilizes artificial intelligence and claim to be reliable. The purpose of this study was to compare the linear and angular cephalometric measurements obtained from web-based fully automated Artificial Intelligence (AI) driven platform “WebCeph”™ with that from manual tracing and evaluate the validity and reliability of automated cephalometric measurements obtained from “WebCeph”™. METHODS: Thirty pre-treatment lateral cephalograms of patients were randomly selected. For manual tracing, digital images of same cephalograms were printed using compatible X-ray printer. After calibration, a total of 18 landmarks was plotted and 12 measurements (8 angular and 4 linear) were obtained using standard protocols. The digital images of each cephalogram were uploaded to “WebCeph”™ server. After image calibration, the automated cephalometric measurements obtained through AI digitization were downloaded for each image. Intraclass correlation coefficient (ICC) was used to determine agreement between the measurements obtained from two methods. ICC value < 0.75 was considered as poor to moderate agreement while an ICC value between 0.75 and 0.90 was considered as good agreement. Agreement was rated as excellent when ICC value > 0.90 was obtained. RESULTS: All the measurements had ICC value above 0.75. A higher ICC value > 0.9 was obtained for seven parameters i.e. ANB, FMA, IMPA/L1 to MP (°), LL to E-line, L1 to NB (mm), L1 to NB (°), S-N to Go-Gn whereas five parameters i.e. UL to E-line, U1 to NA (mm), SNA, SNB, U1 to NA (°) showed ICC value between 0.75 and 0.90. CONCLUSION: A good agreement was found between the cephalometric measurements obtained from “WebCeph”™ and manual tracing. |
format | Online Article Text |
id | pubmed-9020017 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-90200172022-04-21 Evaluation of fully automated cephalometric measurements obtained from web-based artificial intelligence driven platform Mahto, Ravi Kumar Kafle, Dashrath Giri, Abhishek Luintel, Sanjeev Karki, Arjun BMC Oral Health Research BACKGROUND: Artificial Intelligence has created a huge impact in different areas of dentistry. Automated cephalometric analysis is one of the major applications of artificial intelligence in the field of orthodontics. Various automated cephalometric software have been developed which utilizes artificial intelligence and claim to be reliable. The purpose of this study was to compare the linear and angular cephalometric measurements obtained from web-based fully automated Artificial Intelligence (AI) driven platform “WebCeph”™ with that from manual tracing and evaluate the validity and reliability of automated cephalometric measurements obtained from “WebCeph”™. METHODS: Thirty pre-treatment lateral cephalograms of patients were randomly selected. For manual tracing, digital images of same cephalograms were printed using compatible X-ray printer. After calibration, a total of 18 landmarks was plotted and 12 measurements (8 angular and 4 linear) were obtained using standard protocols. The digital images of each cephalogram were uploaded to “WebCeph”™ server. After image calibration, the automated cephalometric measurements obtained through AI digitization were downloaded for each image. Intraclass correlation coefficient (ICC) was used to determine agreement between the measurements obtained from two methods. ICC value < 0.75 was considered as poor to moderate agreement while an ICC value between 0.75 and 0.90 was considered as good agreement. Agreement was rated as excellent when ICC value > 0.90 was obtained. RESULTS: All the measurements had ICC value above 0.75. A higher ICC value > 0.9 was obtained for seven parameters i.e. ANB, FMA, IMPA/L1 to MP (°), LL to E-line, L1 to NB (mm), L1 to NB (°), S-N to Go-Gn whereas five parameters i.e. UL to E-line, U1 to NA (mm), SNA, SNB, U1 to NA (°) showed ICC value between 0.75 and 0.90. CONCLUSION: A good agreement was found between the cephalometric measurements obtained from “WebCeph”™ and manual tracing. BioMed Central 2022-04-19 /pmc/articles/PMC9020017/ /pubmed/35440037 http://dx.doi.org/10.1186/s12903-022-02170-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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, visithttp://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Mahto, Ravi Kumar Kafle, Dashrath Giri, Abhishek Luintel, Sanjeev Karki, Arjun Evaluation of fully automated cephalometric measurements obtained from web-based artificial intelligence driven platform |
title | Evaluation of fully automated cephalometric measurements obtained from web-based artificial intelligence driven platform |
title_full | Evaluation of fully automated cephalometric measurements obtained from web-based artificial intelligence driven platform |
title_fullStr | Evaluation of fully automated cephalometric measurements obtained from web-based artificial intelligence driven platform |
title_full_unstemmed | Evaluation of fully automated cephalometric measurements obtained from web-based artificial intelligence driven platform |
title_short | Evaluation of fully automated cephalometric measurements obtained from web-based artificial intelligence driven platform |
title_sort | evaluation of fully automated cephalometric measurements obtained from web-based artificial intelligence driven platform |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9020017/ https://www.ncbi.nlm.nih.gov/pubmed/35440037 http://dx.doi.org/10.1186/s12903-022-02170-w |
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