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

X-ray Imaging

X-ray Imaging

German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...

Imaging Studies for Cardiovascular System III: X-Ray

Imaging Studies for Cardiovascular System III: X-Ray

The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...

You might also read

Related Articles

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

Sort by

Same author

Radiomic and dosiomic machine learning models for predicting radiation-induced oral mucositis in head and neck cancer: a systematic review.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico·2026

Same author

Squamous Odontogenic Tumor: A Systematic Review.

Oral diseases·2026

Same author

Using Radiomic Features to Detect Anatomical Errors and Assess Deep Learning-Based Left Ventricle Segmentation in Cardiac MRI.

Journal of imaging informatics in medicine·2026

Same author

Nasopalatine duct cyst: a multicenter retrospective study of 63 cases and an integrative review of the clinical, imaginological and histopathological aspects.

Journal of applied oral science : revista FOB·2025

Same author

Radiomorphometric indices for measuring mandibular bone quality in oncologic patients.

Oral radiology·2025

Same author

Efficacy and safety of a new heterologous fibrin biopolymer on socket bone healing after tooth extraction: An experimental pre-clinical study.

Journal of clinical periodontology·2024

Same journal

Kolmogorov-Arnold Guided Local-Global Attention for Medical Image Classification.

Journal of imaging informatics in medicine·2026

Same journal

Artificial Intelligence-Assisted Inner Ear Computed Tomography Analysis: Radiomics-Based Comparison of Affected and Unaffected Ears in Idiopathic Sudden Sensorineural Hearing Loss.

Journal of imaging informatics in medicine·2026

Same journal

High Adoption, Higher Expectations: A Cross-Sectional Survey of Radiologist Engagement with Artificial Intelligence in the United Arab Emirates.

Journal of imaging informatics in medicine·2026

Same journal

Complex-valued Multi-scale Hybrid Attention Network for Fast MRI via Sparsified Data Learning.

Journal of imaging informatics in medicine·2026

Same journal

Automatic Phase and Sequence Identification in Gd-EOB-DTPA-Enhanced Liver MRI Using Deep Convolutional and Sequential Learning.

Journal of imaging informatics in medicine·2026

Same journal

Ultrasound-Based AI in Predicting Hormone Receptor Status in Breast Cancer: Is "Digital Biopsy" Possible.

Journal of imaging informatics in medicine·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 1, 2025

Semi-Automated Planimetric Quantification of Dental Plaque Using an Intraoral Fluorescence Camera

Semi-Automated Planimetric Quantification of Dental Plaque Using an Intraoral Fluorescence Camera

Published on: January 27, 2023

A Systematic Review on Caries Detection, Classification, and Segmentation from X-Ray Images: Methods, Datasets,

Luiz Guilherme Kasputis Zanini¹, Izabel Regina Fischer Rubira-Bullen², Fátima de Lourdes Dos Santos Nunes^3,4

¹Department of Computer Engineering and Digital Systems, University of São Paulo, Av. Prof. Luciano Gualberto 158, São Paulo, 05508-010, São Paulo, Brazil. luiz.kasputis@usp.br.

Journal of Imaging Informatics in Medicine

|March 1, 2024

Summary

This summary is machine-generated.

This systematic review highlights deep learning as the leading computational method for identifying dental caries in X-ray images. The study analyzes 42 articles, emphasizing classification techniques and panoramic imaging.

Keywords:

Caries Cavities Deep learning Dental caries Image processing Machine learning Radiography Systematic review

More Related Videos

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

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

Related Experiment Videos

Last Updated: Jul 1, 2025

Semi-Automated Planimetric Quantification of Dental Plaque Using an Intraoral Fluorescence Camera

Semi-Automated Planimetric Quantification of Dental Plaque Using an Intraoral Fluorescence Camera

Published on: January 27, 2023

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

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:

Computational dentistry
Medical imaging analysis
Artificial intelligence in healthcare

Background:

Dental caries detection from X-ray images is crucial for oral health.
Technological advancements enable sophisticated computational methods for lesion identification.
A systematic review is needed to consolidate current approaches.

Purpose of the Study:

To systematically review and categorize computational methods for dental caries detection in X-ray images.
To identify dominant techniques, imaging modalities, and datasets used in this field.
To understand the strengths, limitations, and trends in computational caries identification.

Main Methods:

Systematic literature review following PRISMA methodology.
Analysis of 42 studies from major scientific databases (Science Direct, IEEExplore, ACM Digital, PubMed).
Categorization of studies based on computational techniques (deep learning, machine learning), task (classification, detection, segmentation), imaging modality, and datasets.

Main Results:

Deep learning is the predominant approach (69% of studies).
Most studies focus on caries classification (76%), primarily binary or multiclass.
Panoramic imaging is the most common radiographic modality (29%).
Only 12% of studies utilized public datasets.

Conclusions:

Deep learning methods show significant promise for automated dental caries detection in radiographic images.
Further research is needed to address challenges related to dataset utilization and standardization.
This review provides a comprehensive overview of computational approaches, guiding future research and development in dental imaging analysis.