Wang Chengkai, Zhang Yifan, Wu Chengyu, Liu Jun, Wu Liuxi, Wang Yitong, Huang Xingru, Feng Xiang, Wang Yaqi
Innovation Center for Electronic Design Automation Technology, Hangzhou Dianzi University, Hangzhou, 310018, China.
College of Media Engineering, Communication University of Zhejiang, Hangzhou, 310018, China.
Sci Data. 2025 Jul 9;12(1):1172. doi: 10.1038/s41597-025-05398-7.
In the rapidly evolving field of dental intelligent healthcare, where Artificial Intelligence (AI) plays a pivotal role, the demand for multimodal datasets is critical. Existing public datasets are primarily composed of single-modal data, predominantly dental radiographs or scans, which limits the development of AI-driven applications for intelligent dental treatment. In this paper, we collect a MultiModal Dental (MMDental) dataset to address this gap. MMDental comprises data from 660 patients, including 3D Cone-beam Computed Tomography (CBCT) images and corresponding detailed expert medical records with initial diagnoses and follow-up documentation. All CBCT scans are conducted under the guidance of professional physicians, and all patient records are reviewed by senior doctors. To the best of our knowledge, this is the first and largest dataset containing 3D CBCT images of teeth with corresponding medical records. Furthermore, we provide a comprehensive analysis of the dataset by exploring patient demographics, prevalence of various dental conditions, and the disease distribution across age groups. We believe this work will be beneficial for further advancements in dental intelligent treatment.
在牙科智能医疗这个快速发展的领域中，人工智能（AI）起着关键作用，对多模态数据集的需求至关重要。现有的公共数据集主要由单模态数据组成，主要是牙科X光片或扫描图像，这限制了用于智能牙科治疗的人工智能驱动应用的发展。在本文中，我们收集了一个多模态牙科（MMDental）数据集来弥补这一差距。MMDental包含来自660名患者的数据，包括三维锥形束计算机断层扫描（CBCT）图像以及相应的详细专家病历，其中有初步诊断和随访记录。所有CBCT扫描均在专业医生的指导下进行，所有患者记录均由资深医生审核。据我们所知，这是第一个也是最大的包含牙齿三维CBCT图像及相应病历的数据集。此外，我们通过探索患者人口统计学特征、各种牙科疾病的患病率以及各年龄组的疾病分布，对该数据集进行了全面分析。我们相信这项工作将有助于牙科智能治疗的进一步发展。
