COMPARISON OF STAINER CEPHALOMETRIC ANALYSIS BETWEEN CONVENTIONAL AND DIGITAL METHODS USING WEBCEPH

Abstract

Background: Cephalometric analysis plays a critical role in orthodontic diagnosis and treatment planning. The identification of anatomical landmarks from lateral cephalograms is crucial for assessing skeletal and dental relationships. Traditionally, cephalometric analysis is performed manually by orthodontists, which is time-consuming and susceptible to inter-observer variability. The integration of artificial intelligence (AI) in cephalometry has the potential to improve diagnostic efficiency and reduce errors. WEBCEPH is an AI-based cephalometric analysis software that automatically detects cephalometric landmarks, allowing for more accurate and efficient analysis compared to traditional manual methods. This study aims to assess the accuracy of AI-based cephalometric analysis using WEBCEPH compared to conventional cephalometric measurement.

Method: This study analyzed 30 lateral cephalometric radiographs with good quality and no dental or craniofacial deformities. Each cephalogram was analyzed using both conventional and digital methods. The Stainer cephalometric skeletal, dental, and soft tissue analyses from both methods were compared using independent t-tests and Mann-whitney.

Outcome: The statistical results indicate that there was no significant difference between conventional and digital methods for all Steiner cephalometric analysis. The WEBCEPH software demonstrated good agreement with conventional methods in cephalometric analysis.

Conclusion: AI-based cephalometric analysis using WEBCEPH provides comparable accuracy to conventional methods, offering a reliable and efficient alternative for orthodontic diagnosis.