Academic Journal
Artificial intelligence-assisted analysis of tongue morphology and sagittal skeletal patterns: A multicenter study.
| Τίτλος: | Artificial intelligence-assisted analysis of tongue morphology and sagittal skeletal patterns: A multicenter study. |
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| Συγγραφείς: | Sun J; Beijing Stomatological Hospital, Capital Medical University, Beijing, China., Zhang Y; Beijing Stomatological Hospital, Capital Medical University, Beijing, China., Su R; Beijing Stomatological Hospital, Capital Medical University, Beijing, China., Yin J; LargeV Instrument Corporation Limited, Beijing, China., Ma Y; Department of Orthodontics, School and Hospital of Stomatology, Shanxi Medical University, Taiyuan, Shanxi, China. Electronic address: blueskyabc1007@163.com., Li S; Beijing Stomatological Hospital, Capital Medical University, Beijing, China. Electronic address: ortholi@126.com. |
| Πηγή: | American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics [Am J Orthod Dentofacial Orthop] 2026 Sep; Vol. 170 (3), pp. 415-424.e1. |
| Τύπος έκδοσης: | Journal Article; Multicenter Study |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier Country of Publication: United States NLM ID: 8610224 Publication Model: Print Cited Medium: Internet ISSN: 1097-6752 (Electronic) Linking ISSN: 08895406 NLM ISO Abbreviation: Am J Orthod Dentofacial Orthop Subsets: MEDLINE |
| Imprint Name(s): | Publication: St. Louis : Elsevier Original Publication: [St. Louis, Mo.] : C.V. Mosby, [c1986- |
| Ιατρικοί όροι (MeSH): | Tongue*/anatomy & histology , Tongue*/diagnostic imaging , Tongue*/pathology , Cephalometry*/methods , Artificial Intelligence* , Deep Learning*, Malocclusion, Angle Class II/pathology ; Malocclusion, Angle Class II/diagnostic imaging ; Malocclusion, Angle Class I/pathology ; Malocclusion, Angle Class I/diagnostic imaging ; Malocclusion, Angle Class III/pathology ; Malocclusion, Angle Class III/diagnostic imaging ; Humans ; Child ; Adolescent ; Female ; Male ; Adult ; Young Adult ; Anatomic Landmarks |
| Περίληψη: | Introduction: This study investigated the association between tongue morphology and sagittal skeletal patterns and assessed a deep learning model for automated tongue landmark detection and segmentation on lateral cephalometric radiographs (LCRs). Methods: Pretreatment LCRs from 503 orthodontic patients (aged 6-40 years) were analyzed, categorized by dentition stage (mixed: 6-11; permanent: 12-40), and sagittal skeletal patterns. Tongue measurements were compared across subgroups. A multicenter dataset of 1179 LCRs trained a deep learning framework integrating U-Net for segmentation and RTMPose (real-time model for pose estimation) for landmark detection: tongue tip, dorsum of tongue, and base of the epiglottis. Model performance was assessed using mean radial error, success detection rate, mean intersection over union, and recall. External validation was performed on an independent hold-out dataset from 2 hospitals. Results: In the permanent dentition group, subjects with Class Ⅲ malocclusion showed larger tongue areas than Class Ⅱ but not Class I; no significant differences in tongue area were observed in the mixed dentition group. The model achieved a mean radial error of 1.72 ± 1.78 mm, success detection rates of 75.65% (≤2 mm) and 91.02% (≤4 mm), a mean intersection over union of 0.907, and a recall of 0.952. Comparable performance was confirmed on external validation. Conclusions: Sagittal skeletal patterns were associated with sagittal tongue area, highlighting the importance of tongue assessment during growth. The proposed deep learning model achieved accurate tongue landmark detection and segmentation, supporting automated cephalometric tongue analysis in orthodontics. (Copyright © 2026 American Association of Orthodontists. Published by Elsevier Inc. All rights reserved.) |
| Entry Date(s): | Date Created: 20260909 Date Completed: 20260909 Latest Revision: 20260921 |
| Update Code: | 20260921 |
| DOI: | 10.1016/j.ajodo.2026.03.011 |
| PMID: | 42716646 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1097-6752 |
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| DOI: | 10.1016/j.ajodo.2026.03.011 |