DENTEX: Dental Enumeration and Tooth Pathosis Detection Benchmark for Panoramic X-ray
Abstract
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
Keywords
Cite
@article{arxiv.2305.19112,
title = {DENTEX: Dental Enumeration and Tooth Pathosis Detection Benchmark for Panoramic X-ray},
author = {Ibrahim Ethem Hamamci and Sezgin Er and Omer Faruk Durugol and Gulsade Rabia Cakmak and Ezequiel de la Rosa and Enis Simsar and Atif Emre Yuksel and Sadullah Gultekin and Serife Damla Ozdemir and Kaiyuan Yang and Mehmet Berke Isler and Mustafa Salih Gucez and Shenxiao Mei and Chenglong Ma and Feihong Shen and Kaidi Shen and Huikai Wu and Han Wu and Lanzhuju Mei and Zhiming Cui and Niels van Nistelrooij and Khalid El Ghoul and Steven Kempers and Tong Xi and Shankeeth Vinayahalingam and Kyoungyeon Choi and Jaewon Shin and Eunyi Lyou and Lanshan He and Yusheng Liu and Lisheng Wang and Tudor Dascalu and Shaqayeq Ramezanzade and Azam Bakhshandeh and Lars Bjørndal and Bulat Ibragimov and Hongwei Bran Li and Sarthak Pati and Bernd Stadlinger and Albert Mehl and Mehmet Kemal Ozdemir and Mustafa Gundogar and Bjoern Menze},
journal= {arXiv preprint arXiv:2305.19112},
year = {2025}
}