MMDental - A multimodal dataset of tooth CBCT images with expert medical records.

Journal: Scientific data
Published Date: Jul 9, 2025

Abstract

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.

Authors

  • Chengkai Wang
    School of Management, Hangzhou Dianzi University, Hangzhou, 310018, China.
  • Yifan Zhang
    Department of Food Science and Nutrition, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang University, Hangzhou, Zhejiang 310058, China.
  • Chengyu Wu
    Department of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, 264200, China.
  • Jun Liu
    Department of Radiology, Second Xiangya Hospital, Changsha, Hunan, China.
  • Liuxi Wu
    Hangzhou Geriatric Stomatology Hospital, Hangzhou Dental Hospital Group, Hangzhou, 310018, China.
  • Yitong Wang
    Department of Pharmacy, Peking University People's Hospital, 11 Xizhimen South Street, Xicheng District, Beijing 100044, China; Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmaceutical Sciences, Peking University, 38 Xueyuan Road, Haidian District, Beijing 100191, China.
  • Xingru Huang
    School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK.
  • Xiang Feng
    Shanghai Engineering Research Center of Digital Education Equipment, East China Normal University, Shanghai 200062, China.
  • Yaqi Wang
    Key Laboratory of RF Circuits and Systems, Ministry of Education, Hangzhou Dianzi University, Hangzhou 310018, China.

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