REAL-Colon:一个数据集,用于在结肠镜检查中开发真实世界的AI应用
Carlo Biffi1, Giulio Antonelli2, Sebastian Bernhofer3,4
1Cosmo Intelligent Medical Devices, Dublin, Ireland. cbiffi@cosmoimd.com.
Scientific data
|May 25, 2024
概括
一个新的数据集,REAL-Colon,提供超过270万高分辨率结肠镜视频与专家注释. 本资源旨在改进人工智能 (AI) 以检测结肠多体和预防癌症.
科学领域:
- 胃肠病学 胃肠病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 检测结肠多重体对于预防结肠直肠癌至关重要.
- 基于人工智能的计算机辅助检测 (CADe) 和诊断 (CADx) 系统在增强结肠镜检查方面表现有前途.
- 现有的数据集缺乏强大的AI开发所需的质量和现实世界的表现.
研究的目的:
- 介绍REAL-Colon数据集,这是一个大规模的,高质量的资源,用于人工智能在结肠镜研究.
- 提供一个全面的数据集,准确地反映现实世界的结肠镜检查程序.
- 促进先进的人工智能算法的开发和基准测试,以检测和诊断多重体.
主要方法:
- 从60个全分辨率的多中心结肠镜录像中编译了270万个原生视频.
- 包括35万个边界框注释,由经验丰富的胃肠病学家监督.
- 综合综合的患者临床,获取和息肉组织病理学数据.
主要成果:
- "REAL-Colon"数据集是对结肠镜AI研究的最大和最高质量的公开资源.
- 它具有全分辨率视频和详细的注释,克服了以前数据集的局限性.
- 数据集的异质性和包括的临床数据支持多样化的AI模型开发.
结论:
- 由于其尺寸,质量和异质性,REAL-Colon是推动人工智能在结肠镜中的独特资源.
- 对此数据集的开放访问促进了对人工智能驱动的多重体检测的严格,可重复的研究.
- 该资源将促进创建更准确和可靠的AI工具,用于结肠镜.
相关概念视频
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