Objectives Deep learning (DL) has been increasingly employed for automated landmark
detection, eg, for cephalometric purposes. We performed a systematic review and meta …

Machine and deep learning algorithms are rapidly growing in dynamic research of medical
imaging. Currently, substantial efforts are developed for the enrichment of medical imaging …

Purpose Accurate segmentation of organs‐at‐risks (OAR s) is the key step for efficient
planning of radiation therapy for head and neck (HaN) cancer treatment. In the work, we …

International challenges have become the standard for validation of biomedical image
analysis methods. Given their scientific impact, it is surprising that a critical analysis of …

Purpose The aim of this investigation was to create an automated cephalometric X-ray
analysis using a specialized artificial intelligence (AI) algorithm. We compared the accuracy …

Objective: To compare the accuracy and computational efficiency of two of the latest deep-
learning algorithms for automatic identification of cephalometric landmarks. Materials and …

Objectives To compare detection patterns of 80 cephalometric landmarks identified by an
automated identification system (AI) based on a recently proposed deep-learning method …

Background Despite the integral role of cephalometric analysis in orthodontics, there have
been limitations regarding the reliability, accuracy, etc. of cephalometric landmarks tracing …

Abstract Background and Objective An accurate lateral cephalometric analysis is vital in
orthodontic diagnosis. Identification of anatomic landmarks on lateral cephalograms is …

Dental radiography plays an important role in clinical diagnosis, treatment and surgery. In
recent years, efforts have been made on developing computerized dental X-ray image …