诊断之外:基于深度学习的医院住院的分析,使用急诊室的腹部X射线图
Yeo Eun Han1, Yongwon Cho2, Beom Jin Park3
1Department of Radiology and Advanced Medical Imaging Institute, Korea University Anam Hospital, Seoul, Republic of Korea.
Abdominal radiology (New York)
|December 20, 2025
概括
使用腹部放射 (AXR) 的深度学习模型可以预测患有腹部症状的急诊室 (ED) 患者的住院风险,对查的敏感性比人类读者更高.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 腹部放射 (AXR) 通常用于急诊室 (ED),但通常具有有限的临床效用.
- 基于AXR的腹部症状患者预测住院风险具有挑战性.
研究的目的:
- 开发和评估深度学习模型,用于预测ED患者患有腹部症状的住院风险,使用AXR.
- 将深度学习模型与人类放射科医生的性能进行比较.
主要方法:
- 一项针对1585名患有腹部症状的成年ED患者进行的回顾性研究,这些患者接受了AXR.
- 开发三种随机森林分类器模型:基于图像的,临床的和融合 (图像+临床特征).
- 在112名患者的外部验证;与两个放射科医生的表现进行比较.
主要成果:
- 融合深度学习模型在内部验证中实现了0.70的AUROC,0.82的灵敏度和0.75的F1得分.
- 深度学习模型表现出比人类读者显著更高的灵敏度和F1分数,尽管特异性较低.
- 外部验证显示性能下降 (AUROC 0.60),但基于图像的模型保留了比人类更高的灵敏度.
结论:
- 深度学习显著提高了AXR在查ED患者住院风险方面的临床实用性.
- 与放射科医生相比,深度学习模型获得了更高的灵敏度和F1分数,表明它们的查潜力.
- 临床实施需要进一步细化模型,但深度学习可以从AXR.提取有价值的预测模式.
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