AIMC Topic: Cephalometry

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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 ...

Automated landmark-based mid-sagittal plane: reliability for 3-dimensional mandibular asymmetry assessment on head CT scans.

Clinical oral investigations
OBJECTIVE: The determination of the mid-sagittal plane (MSP) on three-dimensional (3D) head imaging is key to the assessment of facial asymmetry. The aim of this study was to evaluate the reliability of an automated landmark-based MSP to quantify man...

Digital and artificial intelligence-assisted cephalometric training effectively enhanced students' landmarking accuracy in preclinical orthodontic education.

BMC oral health
BACKGROUND: Digital cephalometric analyses, including those assisted by artificial intelligence (AI), are widely used in clinical practice. Similarly, computer-assisted learning has demonstrated teaching outcomes comparable to those of traditional me...

Machine learning models for improving the diagnosing efficiency of skeletal class I and III in German orthodontic patients.

Scientific reports
The precise and efficient diagnosis of an individual's skeletal class is necessary in orthodontics to ensure correct and stable treatment planning. However, it is difficult to efficiently determine the true skeletal class due to several correlations ...

Predicting changes of incisor and facial profile following orthodontic treatment: a machine learning approach.

Head & face medicine
BACKGROUND: Facial aesthetics is one of major motivations for seeking orthodontic treatment. However, even for experienced professionals, the impact and extent of incisor and soft tissue changes remain largely empirical. With the application of inter...

Deep learning based quantitative cervical vertebral maturation analysis.

Head & face medicine
OBJECTIVES: This study aimed to enhance clinical diagnostics for quantitative cervical vertebral maturation (QCVM) staging with precise landmark localization. Existing methods are often subjective and time-consuming, while deep learning alternatives ...

Classification of skeletal discrepancies by machine learning based on three-dimensional facial scans.

International journal of oral and maxillofacial surgery
The aim of this study was to use machine learning (ML) to classify sagittal and vertical skeletal discrepancies in three-dimensional (3D) facial scans, as well as to evaluate shape variability. 3D facial scans from 435 pre-orthodontic patients were s...

Predicting orthognathic surgery results as postoperative lateral cephalograms using graph neural networks and diffusion models.

Nature communications
Orthognathic surgery, or corrective jaw surgery, is performed to correct severe dentofacial deformities and is increasingly sought for cosmetic purposes. Accurate prediction of surgical outcomes is essential for selecting the optimal treatment plan a...