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...
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...
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 ...
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...
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 ...
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...
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...