BACKGROUND: Accurate shade matching in dentistry is crucial for achieving aesthetic outcomes, with increasing patient expectations driving advancements in shade selection technologies. Color perception is influenced by multiple factors such as incide...
Personal identification of an individual has always been a major concern in forensic science. Reconstruction of the facial profile is considered as one of the final stages in the process of identification. Nevertheless, recent advancements in artific...
With the increasing interest in dental aesthetics, more patients are seeking tooth shade evaluations and whitening treatments. However, traditional methods of visually assessing tooth color with a commercial shade guide are often subjective, emphasiz...
Computer vision, a rapidly advancing branch of artificial intelligence (AI), has gained significant attention in medical and dental applications. Semantic segmentation, a key technique within computer vision, enables the precise identification and de...
Dental characteristics have considerable potential as indicators for estimating chronological age. This study developed a regression model for age estimation using dental characteristics observed in panoramic radiographs. A total of 2,391 radiographs...
Tooth color selection is a crucial step in prosthetic dental treatments. However, the process often suffers from subjectivity, environmental light variability, and the high cost or lack of standardization in instrumental methods. This study aims to d...
OBJECTIVES: Current dental CBCT segmentation tools often lack accuracy, accessibility, or comprehensive anatomical coverage. To address this, we constructed a densely annotated dental CBCT dataset and developed a deep learning model, OraSeg, for toot...
BACKGROUND: Accurate restoration and reconstruction of tooth morphology are crucial in restorative dentistry, implantology, and forensic odontology. Traditional methods, like manual wax modeling and template-based computer-aided design (CAD), struggl...
BACKGROUND: This systematic review and meta-analysis aimed to summarize and evaluate the available information regarding the performance of deep learning methods for tooth detection and segmentation in orthopantomographies.
In the rapidly evolving field of dental intelligent healthcare, where Artificial Intelligence (AI) plays a pivotal role, the demand for multimodal datasets is critical. Existing public datasets are primarily composed of single-modal data, predominant...
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