一个基于人工智能的半定量诊断模型,用于腹内出血,基于对创伤的超声波进行集中评估:一项大型动物实验研究
Chang Liu1, Yang Li2, Hao Tang3
1Department of Emergency Medicine, Daping Hospital, Army Medical University, Chongqing, 400042, China.
Chinese journal of traumatology = Zhonghua chuang shang za zhi
|February 13, 2026
概括
使用对创伤的超声波集中评估 (FAST) 的人工智能模型可以对重创伤患者的腹内出血进行分级. 这种人工智能工具对医院前和资源有限的环境有很大的希望.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 创伤外科 手术 创伤外科
背景情况:
- 的腹部创伤可能导致危及生命的腹内出血.
- 准确的出血分类对于及时干预至关重要,特别是在医院前的环境中.
- 目前用于创伤中出血评估的方法在资源有限的环境中可能是有限的.
研究的目的:
- 开发和验证人工智能模型,用于半定量分级腹内出血.
- 该模型是基于焦点评估与超声波创伤 (FAST) 超声波.
- 目标是使其在医院前或资源有限的环境中使用.
主要方法:
- 用了一种猪模型模拟的腹部创伤,模拟分级出血.
- 快速超声波视频从六个标准网站获得.
- 一个深度神经网络被训练在多式联络特征,包括像素区域和扫描深度,为3级分类.
主要成果:
- 人工智能模型在分级出血方面实现了81.19%的整体准确性.
- 这种性能超过了传统的机器学习模型,如支持向量机和物流回归.
- 在扫描深度和出血像素面积之间发现了显著的负相关性.
结论:
- 使用FAST成像的多式人工智能方法可以有效地对腹内出血进行分级.
- 通过类权重和动态值优化的深度神经网络显示出临床潜力.
- 这项技术可以改善创伤护理中的出血评估,特别是在具有挑战性的环境中.
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