临床医生为内镜AI指导的框架:开发PanEndoAtlas和基准基准模型在整个GI频谱中的基准基准模型
Shreya Johri1, Luyang Luo1, Hong-Yu Zhou1
1Department of Biomedical Informatics, Harvard Medical School, USA.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|February 27, 2026
概括
本研究介绍了PanEndoSuite,这是一个用于胃肠内镜的统一人工智能生态系统,具有大型数据集和基准任务. 新的基础模型PanEndoFM在各种内镜AI挑战中表现出强的表现.
科学领域:
- 人工智能在医学中的应用
- 胃肠病学 胃肠病学
- 医学成像分析 医学成像分析
背景情况:
- 内镜手术对于诊断和管理胃肠道疾病至关重要.
- 目前在内镜中的人工智能发展缺乏大规模,临床多样化的基准和统一的数据集.
- 在这个领域评估视觉基础模型是具有挑战性的,因为数据碎片化.
研究的目的:
- 介绍PanEndoSuite,这是人工智能在胃肠内镜中的第一个统一生态系统.
- 建立一个全面的基准来评估AI模型在临床内镜.
- 开发和评估用于内镜AI应用的新型基础模型.
主要方法:
- 开发了PanEndoSuite,包括PanEndoAtlas (协调数据集),PanEndoX (基准任务) 和PanEndoFM (基础模型).
- 潘恩多阿特拉斯包括超过4.20万张标记图像,对111种胃肠道疾病进行分类.
- PanEndoX具有10个临床基础的任务,而PanEndoFM是在1000万张图像库上进行预训练的.
主要成果:
- 在10个基准任务中,PanEndoFM在6个任务中获得了最高的宏观AUC,证明了广泛的临床概括性.
- 专门的模型在特定领域显示出优势:EndoFM-LV用于结肠任务,EndoSSL用于聚亚型,ViT-B/16用于小肠疾病.
- 这项研究建立了AI模型在各种内镜诊断挑战中的性能基线.
结论:
- PanEndoSuite为在胃肠内镜中开发强大,通用的人工智能系统提供了基础.
- 开发的生态系统弥合了当前人工智能能力和实际临床需求之间的差距.
- 这项工作有助于在临床内镜中推进人工智能驱动的诊断和管理工具.
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