AIMC Topic: Cephalometry
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Developing a fully applicable machine learning (ML) based sex classification model using linear cranial dimensions.
Scientific reports
Dec 28, 2024
Recent advances in artificial intelligence (AI) and machine learning (ML) applications have elevated accomplishments in various scientific fields, primarily those that benefit the economy and society. Contemporary threats, such as armed conflicts, na...
Evaluation of the clinical utility of lateral cephalometry reconstructed from computed tomography extracted by artificial intelligence.
Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery
Dec 9, 2024
This study assessed the accuracy and reliability of artificial intelligence (AI)-reconstructed images of two-dimensional (2D) lateral cephalometric analyses of facial computed tomography (CT) images, which is widely used for the diagnosis of craniofa...
CDSNet: An automated method for assessing growth stages from various anatomical regions in lateral cephalograms based on deep learning.
Journal of the World federation of orthodontists
Nov 22, 2024
BACKGROUND: The assessment of growth stages, typically determined by Cervical Vertebrae Maturation (CVM), plays a crucial role in orthodontics. However, there is a potential deviation from actual growth stages when using CVM. This study aimed to intr...
Artificial intelligence model for predicting sexual dimorphism through the hyoid bone in adult patients.
PloS one
Nov 19, 2024
The objective of this study was to develop a predictive model using supervised machine learning to determine sex based on the dimensions of the hyoid bone. Lateral cephalometric radiographs of 495 patients were analyzed, collecting the horizontal and...
Sex prediction through machine learning utilizing mandibular condyles, coronoid processes, and sigmoid notches features.
PloS one
Nov 15, 2024
Characteristics of the mandible structures have been relevant in anthropological and forensic studies for sex prediction. This study aims to evaluate the coronoid process, condyle, and sigmoid notch patterns in sex prediction through supervised machi...
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...
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