A critical review of artificial intelligence based techniques for automatic prediction of cephalometric landmarks

Abstract Automatic cephalometric landmark detection has emerged as a pivotal area of research that combines medical imaging, computer vision, and orthodontics. The identification of cephalometric landmarks is of utmost importance in the field of orthodontics, as it contributes significantly to the process of diagnosing and planning treatments, as well as conducting research on craniofacial aspects. This practice holds the potential to improve clinical decision-making and ultimately increase the outcomes for patients. This work explores a wide range of strategies, encompassing both traditional edge-based methods and advanced deep learning approaches. The study leveraged various academic publication databases like IEEEXplore, ScienceDirect, arXiv, Springer and PubMed to thoroughly search for articles related to automatic cephalometric landmark detection. Additionally, other pertinent publications were acquired from credible sources like Google Scholar and Wiley databases. Screening the articles relied on three selection criteria: (a) publication titles, abstracts, literature reviews, (b) cephalometric radiograph datasets suitable for 2D landmarking, and (c) studies conducte