Automatic identification of anatomical landmarks in three-dimensional computed tomography/cone-beam computed tomography: a scoping review

Objectives: This study aimed to conduct a scoping review to systematically review automatic identification techniques for soft-tissue and hard-tissue landmarks in three-dimensional (3D)-computed tomography (CT)/cone-beam computed tomography (CBCT), particularly focusing on artificial intelligence (AI)-based methods, to explore the progress and challenges in accuracy, efficiency, and clinical applicability. Materials and methods: In this scoping review, we searched for studies on automatic landmarking in CT/multi-slice spiral CT or CBCT that were published until January 2026 in PubMed, Web of Science, and the Cochrane Library. Studies that validated automatic landmarking methods against a reference standard, typically defined by manual annotations from human experts, were included. Results: In total, 37 (20 CBCT-only, 10 CT-only, and 7 CBCT + CT) studies were included in this review. Most of the studies focused on non-syndromic permanent dentition populations or a single type of malocclusion (except for 6 that involved mixed dentition) and had a limited number of samples, with