Jove
Visualize
Contact Us

Related Concept Videos

Tooth Anatomy01:21

Tooth Anatomy

2.6K
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...
2.6K

You might also read

Related Articles

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

Sort by
Same author

Oral Health Status of Children With a History of Liver Transplantation.

Pediatric transplantation·2025
Same author

Optimization of synchrotron radiation parameters using swarm intelligence and evolutionary algorithms.

Journal of synchrotron radiation·2024
Same author

Cloud type classification using deep learning with cloud images.

PeerJ. Computer science·2024
Same author

Crohn's Disease Prediction Using Sequence Based Machine Learning Analysis of Human Microbiome.

Diagnostics (Basel, Switzerland)·2023
Same author

Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review.

Diagnostics (Basel, Switzerland)·2023
Same author

Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer.

Sensors (Basel, Switzerland)·2023
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
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Mar 18, 2026

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

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

Published on: February 23, 2024

1.6K

Compact Involutional Transformer for Automated Detection of Pediatric Tooth Number Anomalies on Panoramic

Esra Sivari Resul1, Güler Burcu Senirkentli2, Gazi Erkan Bostancı3

  • 1Department of Computer Engineering, Cankiri Karatekin University, Cankiri, 18100, Turkey. esrasivari@karatekin.edu.tr.

Journal of Imaging Informatics in Medicine
|March 17, 2026
PubMed
Summary
This summary is machine-generated.

A new AI model, the Compact Involutional Transformer (CIT), accurately detects tooth number anomalies in children from panoramic X-rays. This automated approach aids in diagnosing conditions like tooth germ deficiency and supernumerary teeth.

Keywords:
Compact involutional transformerDeep learningGerm deficiencyPanoramic radiographsPediatric dentistrySupernumerary teeth

More Related Videos

Author Spotlight: 3D Movement Assessment of Maxillary Posterior Teeth in Clear Aligner Treatment
07:32

Author Spotlight: 3D Movement Assessment of Maxillary Posterior Teeth in Clear Aligner Treatment

Published on: February 23, 2024

2.0K
Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
09:10

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

Published on: August 5, 2021

2.4K

Related Experiment Videos

Last Updated: Mar 18, 2026

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

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

Published on: February 23, 2024

1.6K
Author Spotlight: 3D Movement Assessment of Maxillary Posterior Teeth in Clear Aligner Treatment
07:32

Author Spotlight: 3D Movement Assessment of Maxillary Posterior Teeth in Clear Aligner Treatment

Published on: February 23, 2024

2.0K
Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
09:10

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

Published on: August 5, 2021

2.4K

Area of Science:

  • Dentistry
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Pediatric tooth number anomalies, including germ deficiency and supernumerary teeth, significantly impact occlusion, craniofacial development, and treatment planning.
  • Accurate and timely diagnosis of these anomalies is crucial for effective pediatric dental care.

Purpose of the Study:

  • To introduce and evaluate the Compact Involutional Transformer (CIT), a novel AI model for the automated detection of permanent tooth germ deficiency and supernumerary teeth on pediatric panoramic radiographs.
  • To assess the diagnostic performance of CIT compared to state-of-the-art methods and human expert dentists.

Main Methods:

  • Development of the Compact Involutional Transformer (CIT), a transformer architecture utilizing an adaptive involution-based tokenizer for analyzing pediatric panoramic radiographs.
  • Retrospective collection and curation of 1170 pediatric panoramic radiographs with verified labels by an experienced pediatric dentist.
  • Evaluation of CIT's performance on multi-class (germ deficiency, normal, supernumerary) and binary tasks, including benchmarking against two independent dentist cohorts.

Main Results:

  • CIT achieved high performance in the three-class setting, with 96.00% accuracy, 95.29% F1-score, 95.76% ROC-AUC, and 93.28% Matthews correlation coefficient.
  • The AI model demonstrated significantly higher diagnostic performance than an expert pediatric dentist group.
  • Grad-CAM visualizations were used to examine the model's decision-making process.

Conclusions:

  • The Compact Involutional Transformer (CIT) represents a significant advancement in the automated detection of pediatric tooth number anomalies from panoramic radiographs.
  • This study presents the first AI approach for automatic detection of tooth germ deficiency and the initial application of an involution-based tokenizer in transformers for pediatric dental imaging.
  • CIT shows potential to enhance diagnostic accuracy and efficiency in pediatric dentistry.