OBJECTIVE: To develop and validate a layered deep learning algorithm which automatically creates three-dimensional (3D) surface models of the human mandible out of cone-beam computed tomography (CBCT) imaging.
OBJECTIVES: This study proposed and investigated the performance of a deep learning based three-dimensional (3D) convolutional neural network (CNN) model for automatic segmentation of the pharyngeal airway space (PAS).
OBJECTIVES: We aimed to apply deep learning to detect caries lesions of different radiographic extension on bitewings, hypothesizing it to be significantly more accurate than individual dentists.
OBJECTIVES: Convolutional neural networks (CNNs) are increasingly applied for medical image diagnostics. We performed a scoping review, exploring (1) use cases, (2) methodologies and (3) findings of studies applying CNN on dental image material.
OBJECTIVES: Deep convolutional neural networks (CNNs) are a rapidly emerging new area of medical research, and have yielded impressive results in diagnosis and prediction in the fields of radiology and pathology. The aim of the current study was to e...
OBJECTIVES: This clinical follow-up evaluated the long-term outcome of full-mouth rehabilitations with adhesively bonded all-ceramic restorations in patients suffering from amelogenesis imperfecta (AI) or affected by extensive tooth wear including a ...
OBJECTIVES: This study aims to propose a framework that integrates GPT-4V, a recent advanced version of ChatGPT, and multimodal pre-training techniques to enhance deep learning algorithms for 3-dimensional (3D) tooth segmentation in scans produced by...