| Autor: |
Yunyou Huang, Wenjing Liu, Caiqin Yao, Xiuxia Miao, Xianglong Guan, Xiangjiang Lu, Xiaoshuang Liang, Li Ma, Suqin Tang, Zhifei Zhang, Jianfeng Zhan |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Scientific Data, Vol 11, Iss 1, Pp 1-11 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2052-4463 |
| DOI: |
10.1038/s41597-024-04130-1 |
| Popis: |
Abstract Oral diseases affect nearly 3.5 billion people, and medical resources are limited, which makes access to oral health services nontrivial. Imaging-based machine learning technology is one of the most promising technologies to improve oral medical services and reduce patient costs. The development of machine learning technology requires publicly accessible datasets. However, previous public dental datasets have several limitations: a small volume of computed tomography (CT) images, a lack of multimodal data, and a lack of complexity and diversity of data. These issues are detrimental to the development of the field of dentistry. Thus, to solve these problems, this paper introduces a new dental dataset that contains 169 patients, three commonly used dental image modalities, and images of various health conditions of the oral cavity. The proposed dataset has good potential to facilitate research on oral medical services, such as reconstructing the 3D structure of assisting clinicians in diagnosis and treatment, image translation, and image segmentation. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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