AIMC Topic: Craniofacial Abnormalities
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Machine learning approaches for predicting craniofacial anomalies with graph neural networks.
Computational biology and chemistry
Dec 2, 2024
This study explores the use of machine learning algorithms, including traditional approaches and graph neural networks (GNNs), to predict certain diseases by analyzing protein-protein interactions. Protein-protein interactions (PPIs) are complex, mul...
Artificial Intelligence and Pediatric Otolaryngology.
Otolaryngologic clinics of North America
Jul 20, 2024
Artificial intelligence (AI) studies show how to program computers to simulate human intelligence and perform data interpretation, learning, and adaptive decision-making. Within pediatric otolaryngology, there is a growing body of evidence for the ro...
Deep learning technique to detect craniofacial anatomical abnormalities concentrated on middle and anterior of face in patients with sleep apnea.
Sleep medicine
Sep 24, 2023
OBJECTIVES: The aim of this study is to propose a deep learning-based model using craniofacial photographs for automatic obstructive sleep apnea (OSA) detection and to perform design explainability tests to investigate important craniofacial regions ...
Deep learning is widely applicable to phenotyping embryonic development and disease.
Development (Cambridge, England)
Nov 5, 2021
Genome editing simplifies the generation of new animal models for congenital disorders. However, the detailed and unbiased phenotypic assessment of altered embryonic development remains a challenge. Here, we explore how deep learning (U-Net) can auto...
Automatic Cephalometric Landmark Identification System Based on the Multi-Stage Convolutional Neural Networks with CBCT Combination Images.
Sensors (Basel, Switzerland)
Jan 12, 2021
This study was designed to develop and verify a fully automated cephalometry landmark identification system, based on multi-stage convolutional neural networks (CNNs) architecture, using a combination dataset. In this research, we trained and tested ...
Machine Learning for Identification of Craniomaxillofacial Radiographic Lesions.
Journal of oral and maxillofacial surgery : official journal of the American Association of Oral and Maxillofacial Surgeons
Jul 16, 2020
Deep Geodesic Learning for Segmentation and Anatomical Landmarking.
IEEE transactions on medical imaging
Oct 12, 2018
In this paper, we propose a novel deep learning framework for anatomy segmentation and automatic landmarking. Specifically, we focus on the challenging problem of mandible segmentation from cone-beam computed tomography (CBCT) scans and identificatio...
Second branchial cleft anomalies in children: a literature review.
Pediatric surgery international
Sep 24, 2018
Branchial cleft anomalies are the second most common head and neck congenital lesions in children. It may sometimes be a part of branchio-oto-renal (BOR) syndrome, so in patients with branchial cleft anomalies associated with a complaint of auricular...
Surface scanning and imaging in craniofacial surgery.
Current opinion in otolaryngology & head and neck surgery
Aug 1, 2025
PURPOSE OF REVIEW: This review describes recent innovations in craniofacial imaging, focusing on emerging techniques such as 3D photogrammetry, smartphone-based scanning, and artificial intelligence applied to cephalometric assessments in facial plas...
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