AIMC Topic: Anatomic Landmarks
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Enhancing nasal endoscopy: Classification, detection, and segmentation of anatomic landmarks using a convolutional neural network.
International forum of allergy & rhinology
Jun 10, 2024
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.
Deep Learning-Based Facial and Skeletal Transformations for Surgical Planning.
Journal of dental research
May 29, 2024
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...
Shape completion in the dark: completing vertebrae morphology from 3D ultrasound.
International journal of computer assisted radiology and surgery
May 15, 2024
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 ...
Automatic cephalometric landmark identification with artificial intelligence: An umbrella review of systematic reviews.
Journal of dentistry
May 8, 2024
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...
Artificial intelligence to automate assessment of ocular and periocular measurements.
European journal of ophthalmology
May 6, 2024
PURPOSE: To develop and validate a deep learning facial landmark detection network to automate the assessment of periocular anthropometric measurements.
Deep learning for automatic detection of cephalometric landmarks on lateral cephalometric radiographs using the Mask Region-based Convolutional Neural Network: a pilot study.
Oral surgery, oral medicine, oral pathology and oral radiology
Feb 12, 2024
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).
Knee landmarks detection via deep learning for automatic imaging evaluation of trochlear dysplasia and patellar height.
European radiology
Feb 10, 2024
OBJECTIVES: To develop and validate a deep learning-based approach to automatically measure the patellofemoral instability (PFI) indices related to patellar height and trochlear dysplasia in knee magnetic resonance imaging (MRI) scans.
Accuracy of artificial intelligence-assisted growth prediction in skeletal Class I preadolescent patients using serial lateral cephalograms for a 2-year growth interval.
Orthodontics & craniofacial research
Feb 6, 2024
OBJECTIVE: To investigate the accuracy of artificial intelligence-assisted growth prediction using a convolutional neural network (CNN) algorithm and longitudinal lateral cephalograms (Lat-cephs).
Applications of artificial intelligence in dentomaxillofacial imaging: a systematic review.
Oral surgery, oral medicine, oral pathology and oral radiology
Jan 3, 2024
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...
LOW CERTAINTY OF EVIDENCE SUPPORTS THE APPLICATION OF (AI) FOR THE AUTOMATIC DETECTION OF CEPHALOMETRIC LANDMARKS WITH PROSPECTS FOR IMPROVEMENTS.
The journal of evidence-based dental practice
Dec 21, 2023
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.
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