AIMC Topic: Tooth

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Convolutional neural network for automated tooth segmentation on intraoral scans.

BMC oral health
BACKGROUND: Tooth segmentation on intraoral scanned (IOS) data is a prerequisite for clinical applications in digital workflows. Current state-of-the-art methods lack the robustness to handle variability in dental conditions. This study aims to propo...

YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition.

BMC medical imaging
OBJECTIVES: In the interpretation of panoramic radiographs (PRs), the identification and numbering of teeth is an important part of the correct diagnosis. This study evaluates the effectiveness of YOLO-v5 in the automatic detection, segmentation, and...

Evaluation of the accuracy of automated tooth segmentation of intraoral scans using artificial intelligence-based software packages.

American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics
INTRODUCTION: The accuracy of tooth segmentation in intraoral scans is crucial for performing virtual setups and appliance fabrication. Hence, the objective of this study was to estimate and compare the accuracy of automated tooth segmentation genera...

DentalSegmentator: Robust open source deep learning-based CT and CBCT image segmentation.

Journal of dentistry
OBJECTIVES: Segmentation of anatomical structures on dento-maxillo-facial (DMF) computed tomography (CT) or cone beam computed tomography (CBCT) scans is increasingly needed in digital dentistry. The main aim of this research was to propose and evalu...

Development of Artificial Intelligence Models for Tooth Numbering and Detection: A Systematic Review.

International dental journal
Dental radiography is widely used in dental practices and offers a valuable resource for the development of AI technology. Consequently, many researchers have been drawn to explore its application in different areas. The current systematic review was...

Evaluating tooth segmentation accuracy and time efficiency in CBCT images using artificial intelligence: A systematic review and Meta-analysis.

Journal of dentistry
OBJECTIVES: This systematic review and meta-analysis aimed to assess the current performance of artificial intelligence (AI)-based methods for tooth segmentation in three-dimensional cone-beam computed tomography (CBCT) images, with a focus on their ...

Conquering class imbalances in deep learning-based segmentation of dental radiographs with different loss functions.

Journal of dentistry
OBJECTIVE: The imbalanced nature of real-world datasets is an ongoing challenge in the field of machine and deep learning. In medicine and in dentistry, most data samples represent patients not affected by pathologies, and on imagery, pathologic imag...

Application of deep learning in isolated tooth identification.

BMC oral health
BACKGROUND: Teeth identification has a pivotal role in the dental curriculum and provides one of the important foundations of clinical practice. Accurately identifying teeth is a vital aspect of dental education and clinical practice, but can be chal...

Mandibular and dental measurements for sex determination using machine learning.

Scientific reports
The present study tested the combination of mandibular and dental dimensions for sex determination using machine learning. Lateral cephalograms and dental casts were used to obtain mandibular and mesio-distal permanent teeth dimensions, respectively....

Generative deep learning approaches for the design of dental restorations: A narrative review.

Journal of dentistry
OBJECTIVES: This study aims to explore and discuss recent advancements in tooth reconstruction utilizing deep learning (DL) techniques. A review on new DL methodologies in partial and full tooth reconstruction is conducted.