机器学习和深度学习用于分类脑CT转介的理由
Jaka Potočnik1, Edel Thomas2, Aonghus Lawlor3,4
1University College Dublin School of Medicine, Dublin, Ireland. jaka.potocnik@ucd.ie.
European radiology
|June 24, 2024
概括
机器和深度学习模型可以自动化放射学推理由分析,在多个站点中超越人类专家. 这些人工智能工具提高了对成像指南的遵守,并优化了资源使用.
科学领域:
- 医学成像信息学 医疗成像信息学
- 医疗保健中的人工智能
- 放射学工作流的优化 工作流的优化
背景情况:
- 对放射学转诊的解读非结构化临床指示是复杂的,容易引起人类的变化.
- 目前的理由分析依赖于人类专家,导致潜在的不一致性和低效率.
- 该iGuide分类系统提供了一个框架来评估转介的适当性.
研究的目的:
- 训练机器学习 (ML) 和深度学习 (DL) 模型用于放射学推的自动化理由分析.
- 评估模型在不同临床场所进行概括的能力.
- 将人工智能模型的性能与人类专家判断进行比较.
主要方法:
- 来自三个爱尔兰中心的3000个成年大脑CT转诊的回顾性收集 (2020-2021).
- 放射科医生和咨询放射科医生使用iGuide标准对转诊理由的分析.
- 应用ML和DL技术 (梯度增强,双向LSTM) 对非结构化临床指示进行多类分类.
主要成果:
- 一个渐变增强的ML模型实现了94.4%的准确性和0.94的宏观F1得分.
- 包括双向LSTM在内的深度学习模型的性能略低 (92.3%准确率,0.92宏F1).
- 人类专家的共识是中等的 (放射学家 κ=0.268,放射学家 κ=0.460),表明了显著的变化.
结论:
- 机器和深度学习模型可以有效地自动化放射学推的理由,在临床场所普遍化.
- 基于人工智能的临床指示解释可以在追溯审查中超过人类专家.
- 实施人工智能系统可以改善对成像推指南的遵守,减少辐射剂量,并优化资源配置.
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