AIMC Topic: Cephalometry
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AI-based open-source software for cephalometric analysis from limited FOV radiographs.
Journal of dentistry
Oct 20, 2024
BACKGROUND: Artificial Intelligence (AI) in dental diagnostics is evolving, offering innovative approaches for conducting cephalometric analysis with less manual input and overcoming the limitations of traditional imaging methods. To enhance the diag...
Comparison of semi and fully automated artificial intelligence driven softwares and manual system for cephalometric analysis.
BMC medical informatics and decision making
Sep 27, 2024
BACKGROUND: Cephalometric analysis has been used as one of the main tools for orthodontic diagnosis and treatment planning. The analysis can be performed manually on acetate tracing sheets, digitally by manual selection of landmarks or by recently in...
Predicting newborn birth outcomes with prenatal maternal health features and correlates in the United States: a machine learning approach using archival data.
BMC pregnancy and childbirth
Sep 17, 2024
BACKGROUND: Newborns are shaped by prenatal maternal experiences. These include a pregnant person's physical health, prior pregnancy experiences, emotion regulation, and socially determined health markers. We used a series of machine learning models ...
Classification of cervical vertebral maturation stages with machine learning models: leveraging datasets with high inter- and intra-observer agreement.
Progress in orthodontics
Sep 16, 2024
OBJECTIVES: This study aimed to assess the accuracy of machine learning (ML) models with feature selection technique in classifying cervical vertebral maturation stages (CVMS). Consensus-based datasets were used for models training and evaluation for...
Automatic point detection on cephalograms using convolutional neural networks: A two-step method.
Dental materials journal
Sep 4, 2024
This project aimed to develop an artificial intelligence program tailored for cephalometric images. The program employs a convolutional neural network with 6 convolutional layers and 2 affine layers. It identifies 18 key points on the skull to comput...
Lateral cephalometric parameters among Arab skeletal classes II and III patients and applying machine learning models.
Clinical oral investigations
Sep 3, 2024
BACKGROUND: The World Health Organization considers malocclusion one of the most essential oral health problems. This disease influences various aspects of patients' health and well-being. Therefore, making it easier and more accurate to understand a...
Mandibular Gender Dimorphism: The Utility of Artificial Intelligence and Statistical Shape Modeling in Skeletal Facial Analysis.
Aesthetic plastic surgery
Aug 26, 2024
BACKGROUND: In gender-affirming surgery, facial skeletal dimorphism is an important topic for every craniofacial surgeon. Few cephalometric studies have assessed this topic; however, they fall short to provide skeletal contour insights that direct su...
Comparative analysis of deep-learning-based bone age estimation between whole lateral cephalometric and the cervical vertebral region in children.
The Journal of clinical pediatric dentistry
Jul 3, 2024
Bone age determination in individuals is important for the diagnosis and treatment of growing children. This study aimed to develop a deep-learning model for bone age estimation using lateral cephalometric radiographs (LCRs) and regions of interest (...
Facial soft-tissue shape changes after fixed edgewise treatment with premolar extraction in individual artificial-intelligence-classified facial profile patterns.
BMC oral health
Jun 27, 2024
OBJECTIVE: To examine the patterns of pretreatment facial soft tissue shape in orthodontic cases with premolar extraction using artificial intelligence (AI) and to investigate the corresponding changes.
Deep learning for 3D cephalometric landmarking with heterogeneous multi-center CBCT dataset.
PloS one
Jun 25, 2024
Cephalometric analysis is critically important and common procedure prior to orthodontic treatment and orthognathic surgery. Recently, deep learning approaches have been proposed for automatic 3D cephalometric analysis based on landmarking from CBCT ...
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