Effectiveness of Human-Artificial Intelligence Collaboration in Cephalometric Landmark Detection.

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

SUBMITTER: Le VNT 

PROVIDER: S-EPMC8954049 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature