A Benchmark Dataset for Automated Diagnosis and Treatment Planning of Class III Malocclusion Using X-Rays and Profile Photos

In this paper, we introduce a benchmark dataset for the diagnosis and treatment planning of Class III malocclusion, a condition that requires precise evaluation to determine the necessity of surgical intervention. Our dataset comprises paired lateral cephalometric X-rays and profile photographs, each annotated with a treatment plan mainly indicating whether surgery is required or not. We assess state-of-the-art deep learning models on both imaging modalities to explore their potential for automating diagnosis and treatment decisions. Notably, the dataset facilitates research into non-radiographic diagnostic approaches, potentially enabling treatment planning based solely on profile photographs. We release this dataset as a resource to advance automated applications in orthodontics and maxillofacial surgery.