The rapid advancements in machine learning, graphics processing technologies and the
availability of medical imaging data have led to a rapid increase in the use of deep learning …

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

In this study, we propose a fast and accurate method to automatically localize anatomical
landmarks in medical images. We employ a global-to-local localization approach using fully …

The increasing use of 3-dimensional (3D) imaging by orthodontists and maxillofacial
surgeons to assess complex dentofacial deformities and plan orthognathic surgeries implies …

Artificial intelligence has been emerging as an increasingly important aspect of our daily
lives and is widely applied in medical science. One major application of artificial intelligence …

The lengthy time needed for manual landmarking has delayed the widespread adoption of
three-dimensional (3D) cephalometry. We here propose an automatic 3D cephalometric …

Contrastive Language-Image Pre-training (CLIP), a straightforward yet effective pre-training
paradigm, successfully introduces semantic-rich text supervision to vision models and has …

Objectives The aim of the present systematic review and meta-analysis is to assess the
accuracy of automated landmarking using deep learning in comparison with manual tracing …

Objective. This work investigates the use of deep convolutional neural networks (CNN) to
automatically perform measurements of fetal body parts, including head circumference …

The aim of this systematic review was to assess the accuracy and reliability of automatic
landmarking for cephalometric analysis of three-dimensional craniofacial images. We …