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Panoramic X-rays are frequently used in dentistry for treatment planning, buttheir interpretation can be both time-consuming and prone to error. Artificialintelligence (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 significantchallenges, mainly due to the scarcity of annotated data and variations inanatomical structure. To address these issues, the Dental Enumeration andDiagnosis on Panoramic X-rays Challenge (DENTEX) has been organized inassociation with the International Conference on Medical Image Computing andComputer-Assisted Intervention (MICCAI) in 2023. This challenge aims to promotethe development of algorithms for multi-label detection of abnormal teeth,using three types of hierarchically annotated data: partially annotatedquadrant data, partially annotated quadrant-enumeration data, and fullyannotated quadrant-enumeration-diagnosis data, inclusive of four differentdiagnoses. In this paper, we present the results of evaluating participantalgorithms on the fully annotated data, additionally investigating performancevariation for quadrant, enumeration, and diagnosis labels in the detection ofabnormal teeth. The provision of this annotated dataset, alongside the resultsof this challenge, may lay the groundwork for the creation of AI-powered toolsthat can offer more precise and efficient diagnosis and treatment planning inthe field of dentistry. The evaluation code and datasets can be accessed athttps://github.com/ibrahimethemhamamci/DENTEX

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