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 329 CBCT images from 169 patients, multi-modal data with matching modalities, and images representing various oral health conditions.

口腔疾病影响着近35亿人，其中大多数居住在低收入和中收入国家。由于医疗资源的有限，许多个人无法获得适当的口腔健康护理服务。基于图像的机器学习技术是改善口腔健康护理服务并降低患者成本最有前景的方法之一。公开可访问的数据集在促进机器学习技术的发展中扮演着至关重要的角色。然而，现有的牙科数据集存在诸如锥形束计算机断层扫描（CBCT）数据稀缺、缺乏匹配的多模态数据以及数据复杂性和多样性不足等问题。本项目通过提供一个包含169位患者329张CBCT图像、匹配模态的多模态数据以及代表各种口腔健康状况的图像的数据集，来应对这些挑战。

搜集汇总

背景与挑战

背景概述

该数据集是一个多模态牙科数据集，包含169名患者的329份CBCT图像、8份全景X光片和188份根尖周X光片，支持机器学习在牙科领域的应用，如3D口腔结构重建和牙种植体检测。数据集已通过伦理审查，并提供了详细的数据描述和使用说明。