Evidence map›Paper›PMID 40984697›Full record

ArticleKorean journal of orthodontics2025

Accuracy of artificial intelligence-assisted soft tissue landmark identification in serial lateral cephalograms of Class III two-jaw surgery patients.

Sun Hyong Kim, Inhwan Kim, Jin-Hyoung Cho, Kyung-Hwa Kang, Minji Kim, Su-Jung Kim, Yoon-Ji Kim, Sang-Jin Sung, Young Ho Kim, Sung-Hoon Lim and 3 more

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Article in Korean journal of orthodontics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

13 authors.

Sun Hyong KimDepartment of Orthodontics, School of Dentistry, Kyungpook National University, Daegu, Korea.ORCID https://orcid.org/0009-0008-5533-096X
Inhwan KimDepartment of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0001-7847-6875
Jin-Hyoung ChoDepartment of Orthodontics, Chonnam National University School of Dentistry, Gwangju, Korea.ORCID https://orcid.org/0000-0002-0342-6379
Kyung-Hwa KangDepartment of Orthodontics, School of Dentistry, Wonkwang University, Iksan, Korea.ORCID https://orcid.org/0000-0002-4263-1084
Minji KimDepartment of Orthodontics, College of Medicine, Ewha Womans University, Seoul, Korea.ORCID https://orcid.org/0000-0001-9044-7145
Su-Jung KimDepartment of Orthodontics, Kyung Hee University School of Dentistry, Seoul, Korea.ORCID https://orcid.org/0000-0001-8500-5246
Yoon-Ji KimDepartment of Orthodontics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0002-7030-569X
Sang-Jin SungDepartment of Orthodontics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0001-9593-564X
Young Ho KimDepartment of Orthodontics, Institute of Oral Health Science, Ajou University School of Medicine, Suwon, Korea.ORCID https://orcid.org/0000-0002-4284-1874
Sung-Hoon LimDepartment of Orthodontics, College of Dentistry, Chosun University, Gwangju, Korea.ORCID https://orcid.org/0000-0003-4528-8514
Seung-Hak BaekDepartment of Orthodontics, School of Dentistry, Dental Research Institute, Seoul National University, Seoul, Korea.ORCID https://orcid.org/0000-0002-6586-9503
Namkug KimDepartment of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0002-3438-2217
Mihee HongDepartment of Orthodontics, School of Dentistry, Institute for Translational Research in Dentistry, Kyungpook National University, Daegu, Korea.ORCID https://orcid.org/0000-0001-6015-1482

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To evaluate the accuracy of artificial intelligence (AI)-assisted soft tissue landmark identification (STLI) on serial lateral cephalograms (Lat-Cephs) of Class III patients treated with two-jaw orthognathic surgery across four different time-points. Methods: A convolutional neural network model was developed for STLI, trained and validated using 3,004 Lat-Cephs from 751 patients. The test set included 224 Lat-Cephs from 56 patients, divided into the genioplasty (n = 22) and non-genioplasty (n = 34) groups. The four time-points included initial (T0), pre-surgery (T1, brackets), post-surgery (T2, brackets, surgical plates, and screws [S-PS]), and debonding (T3, S-PS and fixed retainers). AI accuracy was compared with a human standard for 13 soft tissue landmarks. Mean radial errors (MREs), horizontal and vertical errors, and statistical differences were analyzed. Results: The total MRE across all time-points was 1.50 ± 0.48 mm, with 64.9% of values being less than 1.5 mm MRE. There were no significant differences in accuracy among the four time-points (T0, 1.41 mm; T1, 1.53 mm; T2, 1.58 mm; T3, 1.47 mm). The pronasale, stomion inferius (Stmi), stomion superius (Stms) showed an increase in MRE ( Conclusions: The AI algorithm in this study might be an effective tool for STLI in Lat-Cephs at T1, T2, and T3, despite the presence of brackets, S-PS, fixed retainers, genioplasty, and bone remodeling.

Indexed as

Artificial intelligenceLateral cephalogramOrthognathic surgerySoft tissue landmark identification accuracy

Identifiers

PMID40984697
PMCPMC12460026

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.