Automated calibration system for length measurement of lateral cephalometry based on deep learning

Abstract

Objective. Cephalometric analysis has been significantly facilitated by artificial intelligence (AI) in recent years. For digital cephalograms, linear measurements are conducted based on the length calibration process, which has not been automatized in current AI-based systems. Therefore, this study aimed to develop an automated calibration system for lateral cephalometry to conduct linear measurements more efficiently. Approach. This system was based on deep learning algorithms and medical priors of a stable structure, the anterior cranial base (Sella–Nasion). First, a two-stage cascade convolutional neural network was constructed based on 2860 cephalograms to locate sella, nasion, and 2 ruler points in regions of interest. Further, Sella–Nasion distance was applied to estimate the distance between ruler points, and then pixels size of cephalograms was attained for linear measurements. The accuracy of automated landmark localization, ruler length prediction, and linear measurement based on automated calibration was evaluated with statistical analysis. Main results. First, for AI-located points, 99.6% of S and 86% of N points deviated less than 2 mm from the ground truth, and 99% of ruler points deviated less than 0.3 mm from the ground truth. Also, this system correctly predicted the ruler length of 98.95% of samples. Based on automated calibration, 11 linear cephalometric measurements of the test set showed no difference from manual calibration (p > 0.05). Significance. This system was the first reported in the literature to conduct automated calibration with high accuracy and showed high potential for clinical application in cephalometric analysis.