A multimodal dental dataset facilitating machine learning research and clinic services

Oral diseases affect nearly 3.5 billion people, with the majority residing in
low- and middle-income countries. Due to limited healthcare resources, many
individuals are unable to access proper oral healthcare services. Image-based
machine learning technology is one of the most promising approaches to
improving oral healthcare services and reducing patient costs. Openly
accessible datasets play a crucial role in facilitating the development of
machine learning techniques. However, existing dental datasets have
limitations such as a scarcity of Cone Beam Computed Tomography (CBCT) data,
lack of matched multi-modal data, and insufficient complexity and diversity of
the data. This project addresses these challenges by providing a dataset that
includes 574 CBCT images from 389 patients, multi-modal data with matching
modalities, and images representing various oral health conditions.