Comparative Evaluation of an AI-Based Platform (WebCeph™) for Orthodontic Diagnosis in Children and Adolescents: A Retrospective Pilot Study
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Abstract
Background: This retrospective study assessed discrepancies in orthodontic measurements obtained through conventional and AI-based analyses in children (aged 6-13) with mixed dentition. Methodology: Forty-two patients undergoing lateral cephalometric radiographs were included. A single examiner measured 18 landmarks and derived measurements using both methods. Intraclass Correlation Coefficient (ICC) was performed to evaluate intra-examiner reliability. A paired t-test was done to compare the two methods. Results: Seven measurements showed statistically significant differences between the methods: mandibular plane angle, LAFH, facial ratio, upper incisor (U1) to nasion (NA), U1 to A-pogonion (A-Pg), lower incisor (L1) to NB, and L1 to A-pogonion. While the deep learning system offers advantages in speed, responsible application in this age group necessitates a clear understanding of its limitations and incorporation of appropriate evaluation methods into the orthodontic assessment process.
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