Oral surgery, oral medicine, oral pathology and oral radiology
32507560
OBJECTIVE: This investigation aimed to verify and compare the performance of 3 deep learning systems for classifying maxillary impacted supernumerary teeth (ISTs) in patients with fully erupted incisors.
International journal of paediatric dentistry
34904304
BACKGROUND: Supernumerary teeth are a common anomaly and are frequently observed in paediatric patients. To prevent or minimize complications, early diagnosis and treatment is ideal in children with supernumerary teeth.
The aim of the feasibility study was to construct deep learning models for the classification of multiple dental anomalies in panoramic radiographs. Panoramic radiographs with single supernumerary teeth and/or odontomas were considered the "case" gro...
To systematically evaluate artificial intelligence applications for diagnostic and treatment planning possibilities in pediatric dentistry. PubMed, EMBASE, Scopus, Web of Science, IEEE, medRxiv, arXiv, and Google Scholar were searched using specifi...
The aim of this study was to develop an optimal, simple, and lightweight deep learning convolutional neural network (CNN) model to detect the presence of mesiodens on panoramic radiographs. A total of 628 panoramic radiographs with and without mesiod...
OBJECTIVES: This study aimed to develop and validate a robotic system capable of performing accurate and minimally invasive jawbone milling procedures in oral and maxillofacial surgery.