Automatic Identification of Posteroanterior Cephalometric Landmarks using a Novel Deep Learning Algorithm: A Comparative Study with Human Experts

Abstract

Abstract This aimed to propose a fully automatic posteroanterior (PA) cephalometric landmark identification model using deep learning algorithms and evaluate its accuracy and reliability compared with those of expert human examiners. In total, 1,032 PA cephalometric images were used for model training and validation. Two human expert examiners independently and manually identified 19 landmarks on 82 test set images. Similarly, the constructed artificial intelligence (AI) algorithm automatically identified the landmarks on the images. The mean radial error (MRE) and successful detection rate (SDR) were calculated to evaluate the performance of the model. The performance of the model was comparable with that of the examiners. MRE of the model was 1.87 ± 1.53 mm, and SDR was 34.7%, 67.5%, and 91.5% within error ranges of < 1.0, < 2.0, and < 4.0 mm, respectively. The sphenoid points and mastoid processes had the lowest MRE and highest SDR in auto-identification; the condyle points had the highest MRE and lowest SDR. The fully automatic PA cephalometric landmark identification model showed promising accuracy and reliability, comparable with those of the examiners and can help clinicians perform cephalometric analysis more efficiently while saving time and effort. Future advancements in AI could further improve the model accuracy and efficiency.