伦吉内镜下膜剖析视频数据集用于结直肠瘤病变
Jinnan Chen1, Xiangning Zhang2, Jinneng Wang3
1Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Institute of Digestive Disease, Shanghai, China.
这项研究介绍了Renji数据集,这是对结直肠瘤病变 (CNLs) 进行内镜下粘膜切割 (ESD) 视频的新系列. 该资源有助于人工智能在手术视频分析方面的发展,改善外科医生培训和患者护理.
科学领域:
- 医疗人工智能 医疗人工智能
- 手术视频分析手术视频分析
- 胃肠病学 胃肠病学
背景情况:
- 人工智能 (AI) 的进步正在改变外科培训和患者的治疗结果.
- 对于手术阶段识别的深度学习模型需要广泛的注释数据集.
- 内镜下粘膜解剖 (ESD) 是结直肠瘤病变 (CNLs) 的一个关键程序.
研究的目的:
- 介绍Renji数据集,这是首个为CNL提供开放式的ESD视频集合.
- 为开发和验证用于外科视频分析的AI算法提供有价值的资源.
- 建立一个基准,以在未来创建类似的数据集.
主要方法:
- 收集了30个内镜下粘膜解剖 (ESD) 的手术视频,用于结直肠瘤病变 (CNLs).
- 通过三个内镜专家对130,298个视频进行注释,并附有相位特定标签.
- 在Figshare平台上公开提供数据集.
主要成果:
- 伦吉数据集包括30个高质量的ESD程序视频.
- 具有超过13万个阶段特定的注释,用于结直肠瘤病变 (CNLs) 治疗.
- 这一数据集是第一个可以公开访问的数据集.
结论:
- 伦吉数据集对人工智能驱动的外科教育和分析做出了重大贡献.
- 该数据集的公开可访问性将促进内镜手术的研究和开发.
- 这项工作旨在为未来的ESD数据库构建设定标准.
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