基于深度学习的多层次细粒度集成用于手术场景的理解:实验研究
Guangdi Chu1, Yuan Gao2,3, Wei Jiao1,4
1Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
International journal of surgery (London, England)
|September 3, 2025
概括
研究人员创建了一个详细的数据集和人工智能框架用于腹腔镜激进切除术,改善了手术理解,并使得更好的出血预测和自动报告成为可能.
科学领域:
- 在手术中使用计算机视觉
- 医学中的人工智能
- 手术数据科学
背景情况:
- 计算机理解手术场景对于智能手术辅助至关重要.
- 手术现场信息包括整体过程 (粗粒度) 和特定操作 (细粒度) 数据.
- 需要标准化数据集和先进的人工智能来支持临床应用.
研究的目的:
- 构建一个标准化的,全粒度的注释数据集用于腹腔镜彻底切除术.
- 开发一个深度学习框架,以整合多层次的细节信息.
- 通过增强手术场景理解来支持临床智能应用.
主要方法:
- 收集了41个多中心视频并进行了全面的注释 (阶段,步骤,仪器细分,手术三重).
- 设计了一个轻量级的深度学习框架,
- 开发了一个手术内出血预测模型和一个自动化术后总结报告.
主要成果:
- 数据集包含141,443个带有阶段/步骤注释的,8,435个带有仪器细分,和25,305个带有手术三重.
- 深度学习框架在各种细分化任务中表现优于最先进的算法.
- 涉及动脉,静脉和尿管的关键手术步骤被确定为出血风险的关键预测因素.
结论:
- 开发了第一个完全注释的腹腔镜彻底切除术数据集.
- 创建了一个双向增强粗细粒度外科信息的新框架.
- 通过数据算法应用循环为推进智能手术奠定了基础.
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