Cephalometric analysis performance discrepancy between orthodontists and an artificial intelligence model using lateral cephalometric radiographs
Abstract Purpose The purpose of the present clinical study was to compare the Ricketts and Steiner cephalometric analysis obtained by two experienced orthodontists and artificial intelligence (AI)‐based software program and measure the orthodontist variability. Materials and Methods A total of 50 la...
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crwiley:10.1111/jerd.13156 2024-04-21T08:09:58+00:00 Cephalometric analysis performance discrepancy between orthodontists and an artificial intelligence model using lateral cephalometric radiographs Guinot‐Barona, Clara Alonso Pérez‐Barquero, Jorge Galán López, Lidia Barmak, Abdul B. Att, Wael Kois, John C. Revilla‐León, Marta 2023 http://dx.doi.org/10.1111/jerd.13156 https://onlinelibrary.wiley.com/doi/pdf/10.1111/jerd.13156 en eng Wiley http://onlinelibrary.wiley.com/termsAndConditions#vor Journal of Esthetic and Restorative Dentistry volume 36, issue 4, page 555-565 ISSN 1496-4155 1708-8240 General Dentistry journal-article 2023 crwiley https://doi.org/10.1111/jerd.13156 2024-03-28T08:31:03Z Abstract Purpose The purpose of the present clinical study was to compare the Ricketts and Steiner cephalometric analysis obtained by two experienced orthodontists and artificial intelligence (AI)‐based software program and measure the orthodontist variability. Materials and Methods A total of 50 lateral cephalometric radiographs from 50 patients were obtained. Two groups were created depending on the operator performing the cephalometric analysis: orthodontists (Orthod group) and an AI software program (AI group). In the Orthod group, two independent experienced orthodontists performed the measurements by performing a manual identification of the cephalometric landmarks and a software program (NemoCeph; Nemotec) to calculate the measurements. In the AI group, an AI software program (CephX; ORCA Dental AI) was selected for both the automatic landmark identification and cephalometric measurements. The Ricketts and Steiner cephalometric analyses were assessed in both groups including a total of 24 measurements. The Shapiro–Wilk test showed that the data was normally distributed. The t‐test was used to analyze the data ( α = 0.05). Results The t‐test analysis showed significant measurement discrepancies between the Orthod and AI group in seven of the 24 cephalometric parameters tested, namely the corpus length ( p = 0.003), mandibular arc ( p < 0.001), lower face height ( p = 0.005), overjet ( p = 0.019), and overbite ( p = 0.022) in the Ricketts cephalometric analysis and occlusal to SN ( p = 0.002) and GoGn‐SN ( p < 0.001) in the Steiner cephalometric analysis. The intraclass correlation coefficient (ICC) between both orthodontists of the Orthod group for each cephalometric measurement was calculated. Conclusions Significant discrepancies were found in seven of the 24 cephalometric measurements tested between the orthodontists and the AI‐based program assessed. The intra‐operator reliability analysis showed reproducible measurements between both orthodontists, except for the corpus length measurement. ... Article in Journal/Newspaper Orca Wiley Online Library Journal of Esthetic and Restorative Dentistry |
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General Dentistry Guinot‐Barona, Clara Alonso Pérez‐Barquero, Jorge Galán López, Lidia Barmak, Abdul B. Att, Wael Kois, John C. Revilla‐León, Marta Cephalometric analysis performance discrepancy between orthodontists and an artificial intelligence model using lateral cephalometric radiographs |
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General Dentistry |
description |
Abstract Purpose The purpose of the present clinical study was to compare the Ricketts and Steiner cephalometric analysis obtained by two experienced orthodontists and artificial intelligence (AI)‐based software program and measure the orthodontist variability. Materials and Methods A total of 50 lateral cephalometric radiographs from 50 patients were obtained. Two groups were created depending on the operator performing the cephalometric analysis: orthodontists (Orthod group) and an AI software program (AI group). In the Orthod group, two independent experienced orthodontists performed the measurements by performing a manual identification of the cephalometric landmarks and a software program (NemoCeph; Nemotec) to calculate the measurements. In the AI group, an AI software program (CephX; ORCA Dental AI) was selected for both the automatic landmark identification and cephalometric measurements. The Ricketts and Steiner cephalometric analyses were assessed in both groups including a total of 24 measurements. The Shapiro–Wilk test showed that the data was normally distributed. The t‐test was used to analyze the data ( α = 0.05). Results The t‐test analysis showed significant measurement discrepancies between the Orthod and AI group in seven of the 24 cephalometric parameters tested, namely the corpus length ( p = 0.003), mandibular arc ( p < 0.001), lower face height ( p = 0.005), overjet ( p = 0.019), and overbite ( p = 0.022) in the Ricketts cephalometric analysis and occlusal to SN ( p = 0.002) and GoGn‐SN ( p < 0.001) in the Steiner cephalometric analysis. The intraclass correlation coefficient (ICC) between both orthodontists of the Orthod group for each cephalometric measurement was calculated. Conclusions Significant discrepancies were found in seven of the 24 cephalometric measurements tested between the orthodontists and the AI‐based program assessed. The intra‐operator reliability analysis showed reproducible measurements between both orthodontists, except for the corpus length measurement. ... |
format |
Article in Journal/Newspaper |
author |
Guinot‐Barona, Clara Alonso Pérez‐Barquero, Jorge Galán López, Lidia Barmak, Abdul B. Att, Wael Kois, John C. Revilla‐León, Marta |
author_facet |
Guinot‐Barona, Clara Alonso Pérez‐Barquero, Jorge Galán López, Lidia Barmak, Abdul B. Att, Wael Kois, John C. Revilla‐León, Marta |
author_sort |
Guinot‐Barona, Clara |
title |
Cephalometric analysis performance discrepancy between orthodontists and an artificial intelligence model using lateral cephalometric radiographs |
title_short |
Cephalometric analysis performance discrepancy between orthodontists and an artificial intelligence model using lateral cephalometric radiographs |
title_full |
Cephalometric analysis performance discrepancy between orthodontists and an artificial intelligence model using lateral cephalometric radiographs |
title_fullStr |
Cephalometric analysis performance discrepancy between orthodontists and an artificial intelligence model using lateral cephalometric radiographs |
title_full_unstemmed |
Cephalometric analysis performance discrepancy between orthodontists and an artificial intelligence model using lateral cephalometric radiographs |
title_sort |
cephalometric analysis performance discrepancy between orthodontists and an artificial intelligence model using lateral cephalometric radiographs |
publisher |
Wiley |
publishDate |
2023 |
url |
http://dx.doi.org/10.1111/jerd.13156 https://onlinelibrary.wiley.com/doi/pdf/10.1111/jerd.13156 |
genre |
Orca |
genre_facet |
Orca |
op_source |
Journal of Esthetic and Restorative Dentistry volume 36, issue 4, page 555-565 ISSN 1496-4155 1708-8240 |
op_rights |
http://onlinelibrary.wiley.com/termsAndConditions#vor |
op_doi |
https://doi.org/10.1111/jerd.13156 |
container_title |
Journal of Esthetic and Restorative Dentistry |
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1796951224337039360 |