AIMC Topic: Cephalometry

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Accuracy comparative study of automatic landmarking and diagnostic models on lateral cephalograms.

Progress in orthodontics
BACKGROUND: The application of deep learning techniques in cephalometric analysis has become increasingly prominent. Although automatic landmarking models for cephalometric analysis have been developed, their accuracy still requires validation and re...

Comparison of 2D, 2.5D, and 3D landmark localization networks for 3D cephalometry in CT images.

BMC oral health
BACKGROUND: Accurate landmark localization is important for three-dimensional (3D) cephalometric analysis. Although deep learning has shown promising performance for 3D landmark localization, the high computational burden of processing volumetric dat...

Predictive variables analysis for the tongue crib treatment of anterior crossbite in mixed dentition.

BMC oral health
OBJECTIVE: This study aimed to identify key prognostic variables and to develop and validate a clinical prediction model for pre-treatment assessment of tongue crib applicability.

Skel-Net: automatic prediction of skeletal pattern on scanned lateral cephalograms using anatomical prior-guided deep learning network.

BMC oral health
BACKGROUND: Estimating craniofacial patterns is essential for successful orthodontic treatment. However, conventional static measurements are inadequate for capturing dynamic changes, and manual cephalometric analysis is labor-intensive and requires ...

Populational influence on cephalometric landmark identification: performance of two AI-driven software programs in Brazilian and Korean images.

BMC oral health
OBJECTIVE: To assess the performance of cephalometric landmark identification performed by two AI-driven software programs in images from different populations (Brazilian and Korean).

Age estimation of children and adolescents from mandibles using machine learning.

Scientific reports
Age estimation is a crucial step in forensic identification, particularly in scenarios where dental structures may be absent. This study aimed to develop and evaluate supervised machine learning models to predict chronological age based on mandibular...

Evaluation of artificial intelligence-based cephalometric tracing versus semi-automatic and manual tracing.

BMC oral health
BACKGROUND: Artificial intelligence (AI)-based cephalometric tracing has emerged as a promising tool that reduces operator variability and offers standardized, rapid, and reproducible assessments. This study aimed to evaluate the reliability and accu...

Automated classification of skeletal malocclusion in German orthodontic patients.

Clinical oral investigations
OBJECTIVES: Precisely diagnosing skeletal class is mandatory for correct orthodontic treatment. Artificial intelligence (AI) could increase efficiency during diagnostics and contribute to automated workflows. So far, no AI-driven process can differen...

Use of X means and C4.5 algorithms on lateral cephalometric measurements to identify craniofacial patterns.

BMC oral health
BACKGROUND: Craniofacial phenotyping is essential for individualized orthodontic diagnosis and treatment planning. Traditional skeletal classifications, such as the ANB angle, may oversimplify complex relationships among malocclusion types. Machine l...

Multimodal deep learning for cephalometric landmark detection and treatment prediction.

Scientific reports
In orthodontics and maxillofacial surgery, accurate cephalometric analysis and treatment outcome prediction are critical for clinical decision-making. Traditional approaches rely on manual landmark identification, which is time-consuming and subject ...