MIO (My Intraoral Images): Open Access Image Dataset

This record provides the openly accessible version of the image dataset associated with this research.

The dataset is released under Open Access to facilitate reuse, transparency, reproducibility, and compliance with open science principles. This deposit supersedes a previous restricted-access version of the same dataset. Previous restricted-access record: DOI: 10.5281/zenodo.20818533. The scientific content of the dataset remains unchanged; this release only updates the accessibility of the files.

The MIO (My Intraoral Oral Images) dataset is a collection of 765 intraoral clinical photographs prospectively obtained from adult subjects aged 18 to 65. The images were collected as part of a research project focused on the development and validation of artificial intelligence models for the early detection and classification of gingivitis and periodontitis. All images were obtained under standardised clinical conditions using a Canon EOS R50 digital view camera (Canon Inc., Tokyo, Japan). Before inclusion in the dataset, all participants provided informed consent for the use of their anonymised clinical images for research purposes. The study protocol was approved by the relevant Institutional Research and Ethics Committee. The dataset includes images representing three periodontal conditions: dental health, gingivitis, and periodontitis. Each image was independently evaluated and classified by trained dental professionals according to internationally accepted periodontal diagnostic criteria.

Citation: Please cite the associated publication and the Zenodo DOI when using this dataset (10.5281/zenodo.21140854).

Dataset Characteristics
Number of images: 765
Population: Adults (18-65 years)
Image type: Clinical intraoral photographs
Format: JPG
Annotation level: Diagnostic classification at the image level
Categories: Healthy gingiva, Gingivitis and Periodontitis

This dataset can be used for:
Development of artificial intelligence algorithms
Research in deep learning and computer vision
Automated detection of periodontal diseases
Educational purposes in dentistry
Comparative evaluation of machine learning models