The journal of evidence-based dental practice
38821652
ARTICLE TITLE AND BIBLIOGRAPHIC INFORMATION: Artificial Intelligence for Detecting Cephalometric Landmarks: A Systematic Review and Meta-analysis. J Digit Imaging. 2023 Jun;36(3):1158-1179. doi:10.1007/s10278-022-00766-w.
Oral surgery, oral medicine, oral pathology and oral radiology
38637235
BACKGROUND: Artificial intelligence (AI) technology has been increasingly developed in oral and maxillofacial imaging. The aim of this systematic review was to assess the applications and performance of the developed algorithms in different dentomaxi...
Oral surgery, oral medicine, oral pathology and oral radiology
38480069
OBJECTIVE: We examined the effectiveness and feasibility of the Mask Region-based Convolutional Neural Network (Mask R-CNN) for automatic detection of cephalometric landmarks on lateral cephalometric radiographs (LCRs).
The increasing application of virtual surgical planning (VSP) in orthognathic surgery implies a critical need for accurate prediction of facial and skeletal shapes. The craniofacial relationship in patients with dentofacial deformities is still not u...
International journal of computer assisted radiology and surgery
38748052
PURPOSE: Ultrasound (US) imaging, while advantageous for its radiation-free nature, is challenging to interpret due to only partially visible organs and a lack of complete 3D information. While performing US-based diagnosis or investigation, medical ...
OBJECTIVES: The transition from manual to automatic cephalometric landmark identification has not yet reached a consensus for clinical application in orthodontic diagnosis. The present umbrella review aimed to assess artificial intelligence (AI) perf...
PURPOSE: To develop and validate a deep learning facial landmark detection network to automate the assessment of periocular anthropometric measurements.
International forum of allergy & rhinology
38853655
A convolutional neural network (CNN)-based model can accurately localize and segment turbinates in images obtained during nasal endoscopy (NE). This model represents a starting point for algorithms that comprehensively interpret NE findings.
BACKGROUND: The primary surgical approach for removing adrenal masses is minimally invasive adrenalectomy. Recognition of anatomical landmarks during surgery is critical for minimizing complications. Artificial intelligence-based tools can be utilize...