# DENTEX: Dental Enumeration and Tooth Pathosis Detection Benchmark for Panoramic X-ray

arXiv: https://arxiv.org/abs/2305.19112 · HTML: https://mlsanity.com/paper/2305.19112/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2023.

## Built on by (most cited first, 13 of 13)

- [CT2Rep: Automated Radiology Report Generation for 3D Medical Imaging](https://mlsanity.com/paper/2403.06801/) (2024)
- [OPGAgent: An Agent for Auditable Dental Panoramic X-ray Interpretation](https://mlsanity.com/paper/2603.00462/) (2026)
- [PanoGAN A Deep Generative Model for Panoramic Dental Radiographs](https://mlsanity.com/paper/2507.21200/) (2025)
- [Towards Generalist Intelligence in Dentistry: Vision Foundation Models for Oral and Maxillofacial Radiology](https://mlsanity.com/paper/2510.14532/) (2025)
- [OralBBNet: Spatially Guided Dental Segmentation of Panoramic X-Rays with Bounding Box Priors](https://mlsanity.com/paper/2406.03747/) (2024)
- [PanoDiff-SR: Synthesizing Dental Panoramic Radiographs using Diffusion and Super-resolution](https://mlsanity.com/paper/2507.09227/) (2025)
- [Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development](https://mlsanity.com/paper/2603.27460/) (2026)
- [YOLOrtho -- A Unified Framework for Teeth Enumeration and Dental Disease Detection](https://mlsanity.com/paper/2308.05967/) (2023)
- [Intergrated Segmentation and Detection Models for Dentex Challenge 2023](https://mlsanity.com/paper/2308.14161/) (2023)
- [A Sequential Framework for Detection and Classification of Abnormal Teeth in Panoramic X-rays](https://mlsanity.com/paper/2309.00027/) (2023)
- [NAADA: A Noise-Aware Attention Denoising Autoencoder for Dental Panoramic Radiographs](https://mlsanity.com/paper/2506.19387/) (2025)
- [Deep Learning-based Assessment of the Relation Between the Third Molar and Mandibular Canal on Panoramic Radiographs using Local, Centralized, and Federated Learning](https://mlsanity.com/paper/2603.11850/) (2026)
- [Patient-Level, Leakage-Aware Deep Learning for Cross-Center Periapical Radiograph Classification](https://mlsanity.com/paper/2609.14703/) (2026)

## Abstract (arXiv)

Panoramic X-rays are frequently used in dentistry for treatment planning, but their interpretation can be both time-consuming and prone to error. Artificial intelligence (AI) has the potential to aid in the analysis of these X-rays, thereby improving the accuracy of dental diagnoses and treatment plans. Nevertheless, designing automated algorithms for this purpose poses significant challenges, mainly due to the scarcity of annotated data and variations in anatomical structure. To address these issues, we organized the Dental Enumeration and Diagnosis on Panoramic X-rays Challenge (DENTEX) in association with the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) in 2023. This challenge aims to promote the development of algorithms for multi-label detection of abnormal teeth, using three types of hierarchically annotated data: partially annotated quadrant data, partially annotated quadrant-enumeration data, and fully annotated quadrant-enumeration-diagnosis data, inclusive of four different diagnoses. In this paper, we present a comprehensive analysis of the methods and results from the challenge. Our findings reveal that top performers succeeded through diverse, specialized strategies, from segmentation-guided pipelines to highly-engineered single-stage detectors, using advanced Transformer and diffusion models. These strategies significantly outperformed traditional approaches, particularly for the challenging tasks of tooth enumeration and subtle disease classification. By dissecting the architectural choices that drove success, this paper provides key insights for future development of AI-powered tools that can offer more precise and efficient diagnosis and treatment planning in dentistry. The evaluation code and datasets can be accessed at https://github.com/ibrahimethemhamamci/DENTEX