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Manual tracing and artificial intelligence tracing of lateral cephalograms: A critical comparative assessment of performance

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Hayat Q. J., Khan B. R., Khan Z., Kashif M., Zuhair M. A.
Annales Academiae Medicae Silesiensis
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2025
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vol. 79
222-225
EN
INTRODUCTION: Cephalometric analysis, a cornerstone of orthodontics and craniofacial surgery, traditionally involves manual radiograph tracing, a time-consuming and potentially variable process. Artificial intelligence (AI) offers a potential alternative for faster, more consistent analysis. This study compared AI-driven and manual cephalometric methods to assess agreement and identify discrepancies. MATERIAL AND METHODS: This quantitative, comparative cross-sectional study was conducted in a private practice in Peshawar, Pakistan (August–November 2024), including 29 orthodontic patients who met specific criteria (good-quality cephalograms and absence of facial clefts/intra-oral appliances). Cephalometric radiographs were analyzed by two experienced dentists using manual tracing and by AI software (Audaxceph 6.0.50.3887). Five key angular measurements (SNA, SNB, ANB, FMA, and SN-Mp), used in Steiner’s and Tweed’s analyses, were compared. Inter-rater reliability for the manual tracings was assessed using intraclass correlation coefficients (ICCs). RESULTS: Excellent inter-rater reliability was observed for manual tracings (ICCs > 0.90). Paired t-tests revealed no significant differences between manual and AI methods for SNA, SNB, ANB, and FMA. However, a statistically significant difference (p = 0.006) was found for SN-Mp. CONCLUSIONS: This study, comparing manual and AI-driven cephalometric analysis, found strong agreement for most key measurements (SNA, SNB, ANB, and FMA), suggesting AI’s potential to enhance clinical efficiency. The significant difference in SN-Mp, however, emphasizes the need for continued clinical oversight. A combined approach, integrating AI with clinical expertise, is recommended for optimal diagnostic accuracy and treatment planning.
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