深度学习使急性胆囊炎的准确诊断和使用非对比CTCT预测度成为可能
Bai-Qing Chen1,2, Wei Zang3, Jia-Xu Liu4
1Department of Radiology, The People's Hospital of Liaoning Province, No. 33, Wenyi Road, Shenyang, Liaoning 110067, China.
iScience
|December 29, 2025
概括
一个新的深度学习模型准确地诊断急性胆囊炎 (AC) 并使用非对比CT扫描预测其进展为急性性胆囊炎 (ASC). 这种人工智能工具提供了高效率,在临床环境中超越了传统方法.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 胃肠病学 胃肠病学
背景情况:
- 急性胆囊炎 (AC) 是一种常见的外科紧急情况.
- 准确的诊断和预测AC进展到急性性胆囊炎 (ASC) 对于及时干预至关重要.
- 当前的诊断方法可能在速度和准确性方面存在局限性.
研究的目的:
- 开发和验证深度学习 (DL) 模型用于诊断AC.
- 使用非对比计算断层扫描 (CT) 数据预测从AC到ASC的进展.
- 根据放射学和人类专家的评估,评估DL模型的性能.
主要方法:
- 对来自三个医疗中心的641名患者进行了回顾性分析.
- 构建DL模型用于AC诊断和ASC预测.
- 利用梯度加权类激活映射 (Grad-CAM) 进行模型解释性.
- 与放射学模型和放射科医生的性能进行比较.
主要成果:
- DL模型实现了高精度:89.81% (内部) 和81.83% (外部) 的AC诊断.
- ASC预测准确率为84.52% (内部) 和85.60% (外部).
- 综合临床数据的多模式DL模型超过了仅成像模型的性能,并且明显超过了放射学和放射科医生的评估.
结论:
- 一个高效的DL系统可以准确地诊断AC,并预测ASC从非对比CT的进展.
- 与现有方法相比,DL模型显示出更高的性能和计算效率.
- 这种人工智能工具为诊断胆囊炎的时间敏感临床工作流提供了强大的解决方案.
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