PanDent: Toward Comprehensive Tooth-Level Structure-Language Consistency in Dental Radiology
Xiaohan Li, Xinyu Liu, Chang Liu, Kuofeng Hung, Sum Wing Au Yeung, Jun Liu, Yixuan Yuan, Hui Chen
原文摘要
Accurate evaluation of multimodal large language models in dental panoramic radiography is limited by the lack of fine-grained, clinically reliable benchmarks that reflect expert interpretation. This work introduces PanDent, a large-scale benchmark built on expert-validated tooth-level annotations. It comprises 9,554 panoramic radiographs with comprehensive structured findings produced by experienced dentists and further validated by an oral and maxillofacial radiologist. Clinician-guided reports are constructed from these findings using clinician-defined reporting logic, establishing explicit correspondence between structured clinical evidence and free-text descriptions. The dataset supports complementary evaluation of report-level textual agreement and tooth-level clinical consistency, with 9,049 public-source cases for supervised training and an independent 505-case in-house benchmark. Experiments cover proprietary, general-domain open-source, and medical-specific models. Current models generate fluent reports but exhibit substantial errors in fine-grained localization and tooth-level diagnosis. Supervised fine-tuning on PanDent markedly improves report alignment and diagnostic accuracy, bringing predictions closer to expert dental interpretation. These results establish PanDent as a rigorous benchmark and training resource for clinically grounded dental multimodal reasoning. Dataset sample: https://huggingface.co/datasets/Desperado1103/Pandent_Sample; code: https://github.com/HKUDentistry/PanDent. Full data will be released upon publication under source redistribution terms.
cs.CV · cs.MM