DENTEX: An Abnormal Tooth Detection with Dental Enumeration and Diagnosis Benchmark for Panoramic X-rays

• URL: http://arxiv.org/abs/2305.19112v1
• Date: Tue, 30 May 2023 15:15:50 GMT
• Title: DENTEX: An Abnormal Tooth Detection with Dental Enumeration and Diagnosis Benchmark for Panoramic X-rays
• Authors: Ibrahim Ethem Hamamci, Sezgin Er, Enis Simsar, Atif Emre Yuksel, Sadullah Gultekin, Serife Damla Ozdemir, Kaiyuan Yang, Hongwei Bran Li, Sarthak Pati, Bernd Stadlinger, Albert Mehl, Mustafa Gundogar, Bjoern Menze
• Abstract summary: The Dentalion and Diagnosis on Panoramic X-rays Challenge (DENTEX) has been organized in association with the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) in 2023. We present the results of evaluating participant algorithms on the fully annotated data. The provision of this annotated dataset, alongside the results of this challenge, may lay the groundwork for the creation of AI-powered tools in the field of dentistry.
• Score: 0.3355353735901314
• License: http://creativecommons.org/licenses/by/4.0/
• 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, the Dental Enumeration and Diagnosis on Panoramic X-rays Challenge (DENTEX) has been organized 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 the results of evaluating participant algorithms on the fully annotated data, additionally investigating performance variation for quadrant, enumeration, and diagnosis labels in the detection of abnormal teeth. The provision of this annotated dataset, alongside the results of this challenge, may lay the groundwork for the creation of AI-powered tools that can offer more precise and efficient diagnosis and treatment planning in the field of dentistry. The evaluation code and datasets can be accessed at https://github.com/ibrahimethemhamamci/DENTEX

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