  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
