Jove
Visualize
Contact Us
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 Concept Videos

You might also read

Related Articles

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

Sort by
Same author

AI-Generated Synthetic Panoramic Radiograph for Enhanced Dental Image Analysis.

Journal of imaging informatics in medicine·2026
Same author

RibMR - A Mixed Reality Visualization System for Rib Fracture Localization in Surgical Stabilization of Rib Fractures: Phantom, Preclinical, and Clinical Studies.

Journal of imaging informatics in medicine·2024
Same author

Panoramic imaging errors in machine learning model development: a systematic review.

Dento maxillo facial radiology·2024
Same author

Evaluation of the SUCCESS Health Literacy App for Australian Adults With Chronic Kidney Disease: Protocol for a Pragmatic Randomized Controlled Trial.

JMIR research protocols·2022
Same author

COVIDSenti: A Large-Scale Benchmark Twitter Data Set for COVID-19 Sentiment Analysis.

IEEE transactions on computational social systems·2022
Same author

The Checkpoint Program: Collaborative Care to Reduce the Reliance of Frequent Presenters on ED.

International journal of integrated care·2021

Related Experiment Video

Updated: Mar 29, 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

Dental Odontogenic Lesion CBCT and Histopathology Integrated Dataset for Benchmarking Deep Learning Algorithms.

Zimo Huang1, Tian Xia1, Tianfu Wu2,3

  • 1School of Computer Science, The University of Sydney, Sydney, NSW, Australia.

Scientific Data
|March 28, 2026
PubMed
Summary

A new dataset, DOLCHID, pairs cone-beam computed tomography (CBCT) scans with histopathology images for odontogenic lesions. This resource advances artificial intelligence (AI) in dental diagnostics by enabling integrated deep learning models.

More Related Videos

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
Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans
10:23

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Published on: September 8, 2023

3.9K

Related Experiment Videos

Last Updated: Mar 29, 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
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
Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans
10:23

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Published on: September 8, 2023

3.9K

Area of Science:

  • Dentistry
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Accurate diagnosis of odontogenic lesions relies on time-consuming CBCT and histopathology, demanding clinical expertise.
  • Progress in AI for dental diagnostics is limited by a scarcity of combined radiological and histopathological datasets.
  • Existing deep learning models for odontogenic lesions lack integrated multimodal data.

Purpose of the Study:

  • To introduce the Dental Odontogenic Lesion CBCT and Histopathology Integrated Dataset (DOLCHID).
  • To facilitate the development of advanced deep learning models for odontogenic lesion diagnosis.
  • To enable integrative diagnostic modeling leveraging both radiological and histopathological data.

Main Methods:

  • Compilation of 262 paired CBCT scans and H&E-stained histopathology images for four lesion subtypes: dentigerous cyst, radicular cyst, odontogenic keratocyst, and ameloblastoma.
  • Inclusion of expert-verified CBCT segmentation masks and annotated histopathological regions of interest (ROI) for each case.
  • Technical validation of lesion segmentation, single-modality classification, and multimodal classification using the DOLCHID dataset.

Main Results:

  • The DOLCHID dataset comprises 262 paired CBCT and histopathology samples across four major odontogenic lesion types.
  • Technical validations demonstrated the dataset's utility for segmentation, single-modality, and multimodal classification tasks.
  • The dataset facilitates the exploration of deep learning models that integrate complementary information from CBCT and histopathology.

Conclusions:

  • The DOLCHID dataset addresses the critical need for paired radiological and histopathological data in deep learning for odontogenic lesions.
  • This integrated dataset is poised to significantly advance AI-driven diagnostic solutions in dental imaging.
  • DOLCHID will enable the development of more accurate and efficient diagnostic tools by leveraging multimodal data fusion.