🤖 AI Summary
Dental panoramic radiograph interpretation faces challenges including time consumption, high subjectivity, scarcity of annotated data, and substantial anatomical variability—hindering AI-assisted diagnosis. To address these, we introduce the first multi-level benchmark for panoramic radiographs encompassing tooth counting and detection of four dental pathologies, structured hierarchically (quadrant-level, tooth-level enumeration, and diagnostic-level). We propose a hierarchical progressive annotation paradigm and conduct the first systematic evaluation of Transformers and diffusion models for fine-grained dental lesion detection. Our analysis reveals that segmentation-guided detection, single-stage high-precision architectures, and weak/semi-supervised learning significantly improve enumeration accuracy and early lesion identification (F1-score). The proposed methods substantially outperform conventional models. We publicly release full evaluation code and three curated, expert-annotated datasets—establishing the official benchmark for the MICCAI 2023 Dental Panoramic Radiograph Analysis Challenge.
📝 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
