Accuracy of AI-Measured Overjet and Overbite of Cephalometry Compared to Manual Measurement

This study evaluated the reliability of artificial intelligence (AI)-based cephalometric measurements of overjet (OJ) and overbite (OB) and their agreement with manual assessment and investigated factors that may influence measurement outcomes. This retrospective cross-sectional study included 200 orthodontic patients (125 females, 75 males; mean age 20.12 years) selected from 440 records. Manual OJ and OB measurements were obtained twice from dental models using digital calipers and served as the reference standard. AI-based measurements were generated from lateral cephalometric radiographs using WebCeph™. Reliability was assessed using intraclass correlation coefficients (ICCs), while agreement between methods was evaluated using paired t-tests and Bland–Altman analysis. AI-based measurements showed significantly higher OJ and slightly lower OB values than manual measurements, with mean differences of 0.89 mm for OJ and −0.44 mm for OB (p < 0.05). Intra- and inter-examiner reliability were excellent (ICCs > 0.90). Bland–Altman analysis demonstrated that most measurement differences were within the 95% limits of agreement despite a small systematic bias. These findings suggest that AI-assisted cephalometric analysis provides a rapid and reliable approach for routine orthodontic assessment; however, individual discrepancies may be clinically relevant when high measurement precision is required. Therefore, AI should be considered a useful adjunct to conventional assessment, whereas manual measurements remain the reference standard when maximum measurement accuracy is essential.

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Publication Details

Journal
Applied Sciences
Published
2026-07-24
DOI
https://doi.org/10.3390/app16157404
Primary Topic
Dental Radiography and Imaging
Type
article
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article

Accuracy of AI-Measured Overjet and Overbite of Cephalometry Compared to Manual Measurement

Mohamad Talal Radwan, Z Altuğ, Bijan Ganjali
Applied Sciences
Dental Radiography and Imaging
article

Accuracy of AI-Measured Overjet and Overbite of Cephalometry Compared to Manual Measurement

Mohamad Talal Radwan, Z Altuğ, Bijan Ganjali
article en

Abstract

This study evaluated the reliability of artificial intelligence (AI)-based cephalometric measurements of overjet (OJ) and overbite (OB) and their agreement with manual assessment and investigated factors that may influence measurement outcomes. This retrospective cross-sectional study included 200 orthodontic patients (125 females, 75 males; mean age 20.12 years) selected from 440 records. Manual OJ and OB measurements were obtained twice from dental models using digital calipers and served as the reference standard. AI-based measurements were generated from lateral cephalometric radiographs using WebCeph™. Reliability was assessed using intraclass correlation coefficients (ICCs), while agreement between methods was evaluated using paired t-tests and Bland–Altman analysis. AI-based measurements showed significantly higher OJ and slightly lower OB values than manual measurements, with mean differences of 0.89 mm for OJ and −0.44 mm for OB (p < 0.05). Intra- and inter-examiner reliability were excellent (ICCs > 0.90). Bland–Altman analysis demonstrated that most measurement differences were within the 95% limits of agreement despite a small systematic bias. These findings suggest that AI-assisted cephalometric analysis provides a rapid and reliable approach for routine orthodontic assessment; however, individual discrepancies may be clinically relevant when high measurement precision is required. Therefore, AI should be considered a useful adjunct to conventional assessment, whereas manual measurements remain the reference standard when maximum measurement accuracy is essential.

Applied SciencesVol. 16(15)
Near East University (CY)
Openalex Percentile: Top 7%
Dental Radiography and Imaging
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Accuracy of AI-Measured Overjet and Overbite of Cephalometry Compared to Manual Measurement — Mohamad Talal Radwan, Z Altuğ, et al. · Applied Sciences (2026) | TGRS Research Map | TGRS