Reliability of Artificial Intelligence in Lateral Cephalometric Analysis

Document Type : Original articles

Authors

1 Cairo, Egypt

2 Faculty of Dentistry, Ain Shams University, Cairo, Egypt.

Abstract

Objectives: The purpose of this study was to assess the reliability of lateral cephalometric analysis performed by an artificial intelligence-dependent software program.
Methods: One Hundred and Eighty digital cephalometric radiographs acquired by Vatech PaX-i X-ray machine, were used in the study. The anatomical landmarks of both Steiner and McNamara analyses were manually traced using a third-party software AudaxCeph Empower, version 6.6.12.4731 (Audax d.o.o., Ljubljana, Slovenia), the tracing was performed by two radiologists with more than 5 years of experience in digital cephalometry to determine the inter-reliability, then it was repeated with an interval of two weeks to determine the intra-reliability. The landmarks were retraced automatically through the fully automatic option on the same software program using convolutional neural network.
Results: Regarding McNamara analysis, the results of this study showed excellent reliability of the artificial intelligence measurements compared to the manual measurements, with an interclass correlation coefficient >0.9. Regarding Steiner analysis, our results showed excellent reliability of the artificial intelligence measurements compared to the manual measurements (0.75<ICC<1 excluding Positive 1/SN degree, Negative 1i/NB mm, Pg/NB mm, and S-L point mm, which show moderate reliability with 0.4<ICC<0.74). Two measurements showed poor reliability (Holdaway ratio and S-E point mm).
Conclusions: The results of this study showed that the AudaxCeph automated software program has excellent reliability regarding McNamara and Steiner analyses. While in Steiner analysis, manual confirmation should be made with some dental landmarks.

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