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

2025-05-21

AI Summary

Automated diagnosis of dental malocclusion is hindered by the scarcity of large-scale, multi-view, expert-annotated datasets. To address this, we introduce OMNI—the first large-scale, open-source dataset for orthodontic diagnosis—comprising 4,166 images from 384 subjects across three modalities: panoramic radiographs, lateral cephalograms, and intraoral photographs. All images are annotated with standardized anatomical landmarks and clinical diagnostic labels. Leveraging OMNI, we systematically benchmark three mainstream architectures—CNNs, ViT/Swin Transformers, and Graph Neural Networks (GNNs)—and propose a novel multi-view fusion strategy. Experimental results demonstrate substantial improvements in anatomical landmark localization accuracy (+12.7%) and inter-rater diagnostic consistency (Cohen’s κ = 0.89). The dataset, annotation guidelines, and all baseline code are publicly released to establish a foundational benchmark and resource for clinical AI deployment.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multi-instance/Multi-view Learning
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Security and Privacy: Data transparency and provenance

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.

Problem

• Lack of large-scale labeled datasets for malocclusion analysis
• Limited accuracy in automated dental diagnostics
• Need for benchmarks in malocclusion detection research

Innovation

• Introduced OMNI dataset for malocclusion analysis
• Evaluated CNN, Transformer, GNN models on dataset
• Publicly released dataset and baseline code