AIMC Topic: Cephalometry
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Orthodontic craniofacial pattern diagnosis: cephalometric geometry and machine learning.
Medical & biological engineering & computing
Sep 6, 2023
Efficient and reliable diagnosis of craniofacial patterns is critical to orthodontic treatment. Although machine learning (ML) is time-saving and high-precision, prior knowledge should validate its reliability. This study proposed a craniofacial ML d...
The psc-CVM assessment system: A three-stage type system for CVM assessment based on deep learning.
BMC oral health
Aug 12, 2023
BACKGROUND: Many scholars have proven cervical vertebral maturation (CVM) method can predict the growth and development and assist in choosing the best time for treatment. However, assessing CVM is a complex process. The experience and seniority of t...
Novel Machine Learning Algorithms for Prediction of Treatment Decisions in Adult Patients With Class III Malocclusion.
Journal of oral and maxillofacial surgery : official journal of the American Association of Oral and Maxillofacial Surgeons
Jul 26, 2023
BACKGROUND: Management of Class III (Cl III) dentoskeletal phenotype is often expert-driven.
Is automatic cephalometric software using artificial intelligence better than orthodontist experts in landmark identification?
BMC oral health
Jul 8, 2023
BACKGROUND: To evaluate the techniques used for the automatic digitization of cephalograms using artificial intelligence algorithms, highlighting the strengths and weaknesses of each one and reviewing the percentage of success in localizing each ceph...
Human examination and artificial intelligence in cephalometric landmark detection-is AI ready to take over?
Dento maxillo facial radiology
Jul 3, 2023
OBJECTIVES: To compare the precision of two cephalometric landmark identification methods, namely a computer-assisted human examination software and an artificial intelligence program, based on South African data.
Accuracy and clinical validity of automated cephalometric analysis using convolutional neural networks.
Orthodontics & craniofacial research
Jun 16, 2023
BACKGROUND: This study aimed to assess the error range of cephalometric measurements based on the landmarks detected using cascaded CNNs and determine how horizontal and vertical positional errors of individual landmarks affect lateral cephalometric ...
Evaluation of automated detection of head position on lateral cephalometric radiographs based on deep learning techniques.
Annals of anatomy = Anatomischer Anzeiger : official organ of the Anatomische Gesellschaft
Jun 9, 2023
BACKGROUND: Lateral cephalometric radiograph (LCR) is crucial to diagnosis and treatment planning of maxillofacial diseases, but inappropriate head position, which reduces the accuracy of cephalometric measurements, can be challenging to detect for c...
Accuracy of automated 3D cephalometric landmarks by deep learning algorithms: systematic review and meta-analysis.
La Radiologia medica
Apr 24, 2023
OBJECTIVES: The aim of the present systematic review and meta-analysis is to assess the accuracy of automated landmarking using deep learning in comparison with manual tracing for cephalometric analysis of 3D medical images.
Clinical metrics and tools for provider assessment and tracking of trigonocephaly.
Journal of neurosurgery. Pediatrics
Apr 7, 2023
OBJECTIVE: Quantitative measurements of trigonocephaly can be used to characterize and track this phenotype, which is associated with metopic craniosynostosis. Traditionally, trigonocephaly metrics were extracted from CT scans; however, this method e...
Evaluation of an artificial intelligence-based algorithm for automated localization of craniofacial landmarks.
Clinical oral investigations
Apr 4, 2023
OBJECTIVES: Due to advancing digitalisation, it is of interest to develop standardised and reproducible fully automated analysis methods of cranial structures in order to reduce the workload in diagnosis and treatment planning and to generate objecti...
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