MMDental - A multimodal dataset of tooth CBCT images with expert medical records.
Authors
Affiliations (10)
Affiliations (10)
- Innovation Center for Electronic Design Automation Technology, Hangzhou Dianzi University, Hangzhou, 310018, China.
- College of Media Engineering, Communication University of Zhejiang, Hangzhou, 310018, China.
- Hangzhou Geriatric Stomatology Hospital, Hangzhou Dental Hospital Group, Hangzhou, 310018, China.
- School of Medicine and Health Sciences, Lishui University, Lishui, Zhejiang 323000, China.
- Department of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, 264209, China.
- Innovation Center for Electronic Design Automation Technology, Hangzhou Dianzi University, Hangzhou, 310018, China. [email protected].
- Hangzhou Pediatric Stomatology Hospital, Hangzhou, 310000, China.
- College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China.
- Innovation Center for Electronic Design Automation Technology, Hangzhou Dianzi University, Hangzhou, 310018, China. [email protected].
- College of Media Engineering, Communication University of Zhejiang, Hangzhou, 310018, China. [email protected].
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.