Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking

TL;DR

This paper introduces the OMNI dataset, a large-scale, annotated collection of dental images for malocclusion diagnosis, along with baseline deep learning models and benchmarking to advance automated orthodontic diagnostics.

Contribution

The study presents the OMNI dataset, a comprehensive, annotated dental image dataset, and provides baseline deep learning methods and benchmarking for malocclusion diagnosis.

Findings

OMNI dataset contains 4166 images from 384 participants.

Deep learning models achieve promising diagnostic accuracy on OMNI.

Benchmark results establish a new standard for future research.

Abstract

Malocclusion is a major challenge in orthodontics, and its complex presentation and diverse clinical manifestations make accurate localization and diagnosis particularly important. Currently, one of the major shortcomings facing the field of dental image analysis is the lack of large-scale, accurately labeled datasets dedicated to malocclusion issues, which limits the development of automated diagnostics in the field of dentistry and leads to a lack of diagnostic accuracy and efficiency in clinical practice. Therefore, in this study, we propose the Oral and Maxillofacial Natural Images (OMNI) dataset, a novel and comprehensive dental image dataset aimed at advancing the study of analyzing dental images for issues of malocclusion. Specifically, the dataset contains 4166 multi-view images with 384 participants in data collection and annotated by professional dentists. In addition, we performed a comprehensive validation of the created OMNI dataset, including three CNN-based methods, two Transformer-based methods, and one GNN-based method, and conducted automated diagnostic experiments for malocclusion issues. The experimental results show that the OMNI dataset can facilitate the automated diagnosis research of malocclusion issues and provide a new benchmark for the research in this field. Our OMNI dataset and baseline code are publicly available at https://github.com/RoundFaceJ/OMNI.