Abstract
Detection of cephalometric landmarks has contributed to the analysis of malocclusion during orthodontic diagnosis. Many recent studies involving deep learning have focused on head-to-head comparisons of accuracy in landmark identification between artificial intelligence (AI) and humans. However, a human–AI collaboration for the identification of cephalometric landmarks has not been evaluated. We selected 1193 cephalograms and used them to train the deep anatomical context feature learning (DACFL) model. The number of target landmarks was 41. To evaluate the effect of human–AI collaboration on landmark detection, 10 images were extracted randomly from 100 test images. The experiment included 20 dental students as beginners in landmark localization. The outcomes were determined by measuring the mean radial error (MRE), successful detection rate (SDR), and successful classification rate (SCR). On the dataset, the DACFL model exhibited an average MRE of 1.87 ± 2.04 mm and an average SDR of 73.17% within a 2 mm threshold. Compared with the beginner group, beginner–AI collaboration improved the SDR by 5.33% within a 2 mm threshold and also improved the SCR by 8.38%. Thus, the beginner–AI collaboration was effective in the detection of cephalometric landmarks. Further studies should be performed to demonstrate the benefits of an orthodontist–AI collaboration.
Highlights
IntroductionDetection of cephalometric landmarks refers to the localization of anatomical landmarks of the skull and surrounding soft tissues on lateral cephalograms
In orthodontics, detection of cephalometric landmarks refers to the localization of anatomical landmarks of the skull and surrounding soft tissues on lateral cephalograms.Since the introduction of lateral cephalograms by Broadbent and Hofrath in 1931, this approach has contributed to the analysis of malocclusion and has become a standardized diagnostic method in orthodontic practice and research [1]
The deep anatomical context feature learning (DACFL) model showed an average mean radial error (MRE) of 1.87 ± 2.04 mm (Table 2)
Summary
Detection of cephalometric landmarks refers to the localization of anatomical landmarks of the skull and surrounding soft tissues on lateral cephalograms. Deep learning-based reports using convolutional neural networks (CNNs) have achieved remarkable results [2–5]. Despite the limited number of annotated cephalograms, many CNN-based approaches have been proposed to solve the problem associated with the detection of anatomical landmarks. To address the restricted availability of medical imaging data for network learning with respect to the localization of anatomical landmarks, Zhang et al proposed a two-stage task-oriented deep neural network method [7]. Oh et al proposed the deep anatomical context feature learning (DACFL) model, which employs a Laplace heatmap regression method based on a fully convolutional network. LFP can be considered a data augmentation method based on prior anatomical knowledge It perturbs the local pattern of the cephalogram, forcing the network to seek relevant features globally. Among the previous CNN models, DACFL outperformed other state-of-the-art methods and achieved high performance in landmark identification on the IEEE ISBI 2015 dataset [2]. We used a private dataset to evaluate the performance of the DACFL model in clinical applications
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