通过使用卷积神经网络的增强计算机断层扫描来预测急性胰腺炎的严重程度
Hongyin Liang1,2, Meng Wang3, Yi Wen1,2
1Department of General Surgery, The General Hospital of Western Theater Command (Chengdu Military General Hospital), Chengdu, 610083, China.
Scientific reports
|October 16, 2023
概括
卷积神经网络 (CNN) 模型使用增强型计算机断层扫描 (CT) 扫描准确预测急性胰腺炎 (AP) 严重程度. 这种人工智能方法显示了改善AP诊断和管理的前景.
科学领域:
- 放射学和医学成像学 医学成像学
- 人工智能在医学中的应用
- 计算病理学计算病理学
背景情况:
- 评估急性胰腺炎 (AP) 严重程度对于患者管理至关重要.
- 目前用于AP严重程度评估的方法可能是主观的,耗时的.
- 先进的成像技术和机器学习为客观评估提供了潜力.
研究的目的:
- 评估卷积神经网络 (CNN) 模型在预测AP严重性的有效性.
- 利用增强的计算机断层扫描 (CT) 扫描来进行人工智能驱动的严重程度分类.
- 用两个不同的AP严重程度分级系统来比较CNN的表现.
主要方法:
- 开发和培训使用增强型CT扫描的3DDDenseNet CNN模型.
- 独立的模型培训和验证使用CT扫描标有计算机断层扫描严重性指数 (CTSI) 和亚特兰大分类.
- 通过混矩阵,AUC-ROC,准确性,精度,回忆和F1分数进行性能评估.
主要成果:
- 对于这两种标签方法,DenseNet模型的准确度>0.7和AUC-ROC>0.8.
- 用CTSI标记扫描训练的模型显示宏观平均F1得分为0.835和AUC-ROC为0.980.
- 该研究包括1798个增强型CT扫描,随机分为训练 (n=1618) 和测试 (n=180) 组.
结论:
- 美国有线电视新闻网 (CNN) 的模型,特别是DenseNet,显示出从增强型CT扫描中预测AP严重性的显著可行性.
- 对CT扫描的AI驱动分析为客观AP严重性评估提供了可靠的方法.
- 这些发现支持将人工智能工具集成到临床放射学工作流程中,用于AP管理.
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