Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Tooth Anatomy

Tooth Anatomy

The human tooth enables us to eat a variety of foods, speak clearly, and even aid in shaping our faces. Teeth are composed of various elements that work together. Here's a detailed look at the anatomy of a human tooth.
The Crown, Neck, and Root
The visible part of the tooth is referred to as the crown. It's covered by enamel, the hardest substance in the human body. The crown is uniquely shaped for each type of tooth, allowing for different functions such as cutting, tearing, or grinding food.

Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

Imaging Studies III: Computed Tomography

Imaging Studies III: Computed Tomography

DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

A Novel ConvNeXtV2-MIL Approach for Accurate and Efficient Classification of Microscopic Fungal Morphology.

Journal of imaging informatics in medicine·2026

Same author

Advancing Drug-Drug Interaction Prediction with Biomimetic Improvements: Leveraging the Latest Artificial Intelligence Techniques to Guide Researchers in the Field.

Biomimetics (Basel, Switzerland)·2026

Same author

Classification of Apical Openness Using Vision Transformer: A Comparative Approach with Expert Decisions.

Journal of imaging informatics in medicine·2025

Same author

Ensemble-Based Early Detection of Malaria via Explainable ViT-CNN Feature Fusion and SHAP.

Journal of imaging informatics in medicine·2025

Same author

NCA-EVA: An Innovative Ensemble-Based Approach for Alzheimer's Disease Detection from Magnetic Resonance Imaging.

Journal of imaging informatics in medicine·2025

Same author

Evaluation of Apical Closure in Panoramic Radiographs Using Vision Transformer Architectures ViT-Based Apical Closure Classification.

Diagnostics (Basel, Switzerland)·2025

Search research articles

Related Experiment Video

Updated: Jun 17, 2026

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Published on: February 23, 2024

Automated Detection of Taurodontism in Panoramic Radiographs Using a YOLOv8-Based Deep Learning Model.

Merve Hacer Talu¹, Sümeyye Coşgun-Baybars², Rawan Aboalqaraya³

¹Department of Oral and Maxillofacial Radiology, Hamidiye Faculty of Dentistry, University of Health Sciences, Istanbul, Turkey. mervehacer.talu@sbu.edu.tr.

Journal of Imaging Informatics in Medicine

|June 15, 2026

Summary

This summary is machine-generated.

This study developed an AI system using YOLOv8 for automated taurodontism detection on dental radiographs. The YOLOv8 medium model showed high accuracy, improving diagnostic consistency in dental imaging.

Keywords:

Computer-aided diagnosis Deep learning Medical imaging informatics Panoramic radiography Taurodontism YOLOv8

More Related Videos

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

Related Experiment Videos

Last Updated: Jun 17, 2026

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Published on: February 23, 2024

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

Area of Science:

Dentistry
Medical Imaging
Artificial Intelligence

Background:

Taurodontism is a developmental anomaly of molar teeth.
Accurate detection is crucial for diagnosis and treatment planning.
Current detection methods can be time-consuming and subjective.

Purpose of the Study:

To develop and evaluate a deep learning system for automated taurodontism detection.
To utilize the You Only Look Once version 8 (YOLOv8) architecture.
To compare the diagnostic performance of different YOLOv8 model variants.

Main Methods:

Retrospective analysis of 247 panoramic radiographs (1631 molar teeth).
Classification based on the Shifman and Chanannel taurodontism index.
Training and evaluation of three YOLOv8 variants (nano, small, medium).
Performance metrics included mAP50, mAP50-95, precision, recall, F1-score, and inference time.

Main Results:

The YOLOv8 medium model achieved the highest performance (mAP50: 0.988, mAP50-95: 0.849).
The small model offered a balance of accuracy and efficiency.
The nano model provided rapid inference with reduced accuracy.
Qualitative analysis confirmed accurate localization and classification.

Conclusions:

YOLOv8-based models demonstrate high diagnostic accuracy for automated taurodontism detection.
This AI approach can enhance diagnostic consistency and workflow efficiency.
Supports the integration of AI into routine dental imaging practices.