Evaluation of automated photograph-cephalogram image integration using artificial intelligence models.

Abstract

Objectives: To develop and evaluate an automated method for combining a digital photograph with a lateral cephalogram.

Materials and methods: A total of 985 digital photographs were collected and soft tissue landmarks were manually detected. Then 2500 lateral cephalograms were collected, and corresponding soft tissue landmarks were manually detected. Using the images and landmark identification information, two different artificial intelligence (AI) models-one for detecting soft tissue on photographs and the other for identifying soft tissue on cephalograms-were developed using different deep-learning algorithms. The digital photographs were rotated, scaled, and shifted to minimize the squared sum of distances between the soft tissue landmarks identified by the two different AI models. As a validation process, eight soft tissue landmarks were selected on digital photographs and lateral cephalometric radiographs from 100 additionally collected validation subjects. Paired t-tests were used to compare the accuracy of measures obtained between the automated and manual image integration methods.

Results: The validation results showed statistically significant differences between the automated and manual methods on the upper lip and soft tissue B point. Otherwise, no statistically significant difference was found.

Conclusions: Automated photograph-cephalogram image integration using AI models seemed to be as reliable as manual superimposition procedures.