Artificial intelligence in orthodontics : Evaluation of a fully automated cephalometric analysis using a customized convolutional neural network

Abstract

Purpose: The aim of this investigation was to create an automated cephalometric X‑ray analysis using a specialized artificial intelligence (AI) algorithm. We compared the accuracy of this analysis to the current gold standard (analyses performed by human experts) to evaluate precision and clinical application of such an approach in orthodontic routine.

Methods: For training of the network, 12 experienced examiners identified 18 landmarks on a total of 1792 cephalometric X‑rays. To evaluate quality of the predictions of the AI, both AI and each examiner analyzed 12 commonly used orthodontic parameters on a basis of 50 cephalometric X‑rays that were not part of the training data for the AI. Median values of the 12 examiners for each parameter were defined as humans' gold standard and compared to the AI's predictions.

Results: There were almost no statistically significant differences between humans' gold standard and the AI's predictions. Differences between the two analyses do not seem to be clinically relevant.

Conclusions: We created an AI algorithm able to analyze unknown cephalometric X‑rays at almost the same quality level as experienced human examiners (current gold standard). This study is one of the first to successfully enable implementation of AI into dentistry, in particular orthodontics, satisfying medical requirements.

Keywords: Algorithms; Cephalometric X‑rays; Deep learning; Machine learning; Medical imaging.