Comparing intra-observer variation and external variations of a fully automated cephalometric analysis with a cascade convolutional neural net

Abstract

The quality of cephalometric analysis depends on the accuracy of the delineating landmarks in orthodontic and maxillofacial surgery. Due to the extensive number of landmarks, each analysis costs orthodontists considerable time per patient, leading to fatigue and inter- and intra-observer variabilities. Therefore, we proposed a fully automated cephalometry analysis with a cascade convolutional neural net (CNN). One thousand cephalometric x-ray images (2 kx3 k) pixel were used. The dataset was split into training, validation, and test sets as 8:1:1. The 43 landmarks from each image were identified by an expert orthodontist. To evaluate intra-observer variabilities, 28 images from the dataset were randomly selected and measured again by the same orthodontist. To improve accuracy, a cascade CNN consisting of two steps was used for transfer learning. In the first step, the regions of interest (ROIs) were predicted by RetinaNet. In the second step, U-Net detected the precise landmarks in the ROIs. The average error of ROI detection alone was 1.55 +/- 2.17 mm. The model with the cascade CNN showed an average error of 0.79 +/- 0.91 mm (paired t-test, p=0.0015). The orthodontist's average error of reproducibility was 0.80 +/- 0.79 mm. An accurate and fully automated cephalometric analysis was successfully developed and evaluated.