AI Medical Compendium Topic

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Tooth

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ChatIOS: Improving automatic 3-dimensional tooth segmentation via GPT-4V and multimodal pre-training.

Journal of dentistry
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...

TSegLab: Multi-stage 3D dental scan segmentation and labeling.

Computers in biology and medicine
This study introduces a novel deep learning approach for 3D teeth scan segmentation and labeling, designed to enhance accuracy in computer-aided design (CAD) systems. Our method is organized into three key stages: coarse localization, fine teeth segm...

Enhancing the classification of isolated theropod teeth using machine learning: a comparative study.

PeerJ
Classifying objects, such as taxonomic identification of fossils based on morphometric variables, is a time-consuming process. This task is further complicated by intra-class variability, which makes it ideal for automation via machine learning (ML) ...

ML-based tooth shade assessment to prevent metamerism in different clinic lights.

Lasers in medical science
The aesthetic understanding has found its place in dental clinics and prosthetic dental treatment. Determining the appropriate prosthetic tooth color between the clinician, patient and technician is a difficult process due to metamerism. Metamerism, ...

A multi-modal dental dataset for semi-supervised deep learning image segmentation.

Scientific data
In response to the increasing prevalence of dental diseases, dental health, a vital aspect of human well-being, warrants greater attention. Panoramic X-ray images (PXI) and Cone Beam Computed Tomography (CBCT) are key tools for dentists in diagnosing...

A novel deep learning-based model for automated tooth detection and numbering in mixed and permanent dentition in occlusal photographs.

BMC oral health
BACKGROUND: While artificial intelligence-driven approaches have shown great promise in dental diagnosis and treatment planning, most research focuses on dental radiographs. Only three studies have explored automated tooth numbering in oral photograp...

Assessment of CNNs, transformers, and hybrid architectures in dental image segmentation.

Journal of dentistry
OBJECTIVES: Convolutional Neural Networks (CNNs) have long dominated image analysis in dentistry, reaching remarkable results in a range of different tasks. However, Transformer-based architectures, originally proposed for Natural Language Processing...

Accuracy of artificial intelligence-based segmentation in maxillofacial structures: a systematic review.

BMC oral health
OBJECTIVE: The aim of this review was to evaluate the accuracy of artificial intelligence (AI) in the segmentation of teeth, jawbone (maxilla, mandible with temporomandibular joint), and mandibular (inferior alveolar) canal in CBCT and CT scans.

3D tooth identification for forensic dentistry using deep learning.

BMC oral health
The classification of intraoral teeth structures is a critical component in modern dental analysis and forensic dentistry. Traditional methods, relying on 2D imaging, often suffer from limitations in accuracy and comprehensiveness due to the complex ...

Evaluating masked self-supervised learning frameworks for 3D dental model segmentation tasks.

Scientific reports
The application of deep learning using dental models is crucial for automated computer-aided treatment planning. However, developing highly accurate models requires a substantial amount of accurately labeled data. Obtaining this data is challenging, ...