MMDental - A multimodal dataset of tooth CBCT images with expert medical records
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 lim...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05398-7 |
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| _version_ | 1849237993842278400 |
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| author | Chengkai Wang Yifan Zhang Chengyu Wu Jun Liu Xingliang Huang Liuxi Wu Yitong Wang Xiang Feng Yiting Lu Yaqi Wang |
| author_facet | Chengkai Wang Yifan Zhang Chengyu Wu Jun Liu Xingliang Huang Liuxi Wu Yitong Wang Xiang Feng Yiting Lu Yaqi Wang |
| author_sort | Chengkai Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-850e09fdb2bc4ae28f09146c8325dd42 |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-850e09fdb2bc4ae28f09146c8325dd422025-08-20T04:01:47ZengNature PortfolioScientific Data2052-44632025-07-0112111110.1038/s41597-025-05398-7MMDental - A multimodal dataset of tooth CBCT images with expert medical recordsChengkai Wang0Yifan Zhang1Chengyu Wu2Jun Liu3Xingliang Huang4Liuxi Wu5Yitong Wang6Xiang Feng7Yiting Lu8Yaqi Wang9School of Management, Hangzhou Dianzi UniversityHangzhou Geriatric Stomatology Hospital, Hangzhou Dental Hospital GroupDepartment of Mechanical, Electrical and Information Engineering, Shandong UniversityInnovation Cesnter for Electronic Design Automation Technology, Hangzhou Dianzi UniversityHangzhou Pediatric Stomatology HospitalHangzhou Geriatric Stomatology Hospital, Hangzhou Dental Hospital GroupInnovation Cesnter for Electronic Design Automation Technology, Hangzhou Dianzi UniversityCollege of Computer Science and Technology, Hangzhou Dianzi UniversitySchool of Economics, Hangzhou Dianzi UniversityInnovation Cesnter for Electronic Design Automation Technology, Hangzhou Dianzi UniversityAbstract 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.https://doi.org/10.1038/s41597-025-05398-7 |
| spellingShingle | Chengkai Wang Yifan Zhang Chengyu Wu Jun Liu Xingliang Huang Liuxi Wu Yitong Wang Xiang Feng Yiting Lu Yaqi Wang MMDental - A multimodal dataset of tooth CBCT images with expert medical records Scientific Data |
| title | MMDental - A multimodal dataset of tooth CBCT images with expert medical records |
| title_full | MMDental - A multimodal dataset of tooth CBCT images with expert medical records |
| title_fullStr | MMDental - A multimodal dataset of tooth CBCT images with expert medical records |
| title_full_unstemmed | MMDental - A multimodal dataset of tooth CBCT images with expert medical records |
| title_short | MMDental - A multimodal dataset of tooth CBCT images with expert medical records |
| title_sort | mmdental a multimodal dataset of tooth cbct images with expert medical records |
| url | https://doi.org/10.1038/s41597-025-05398-7 |
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