基于深度学习的腹部放射学特征模型在新生儿早期死性肠球炎诊断方面表现优于专家
Yu Wu1, Hao Yang2, Xiaomei Luo1
1Department of Radiology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
European radiology
|January 13, 2026
概括
应用到腹部放射学中的深度学习模型显著改善了第一阶段新生儿死性肠球炎 (NEC) 的早期诊断. 与人类专家相比,这些人工智能工具的准确性更高,为临床决策提供了有价值的非侵入性帮助.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 新生儿护理 新生儿护理
背景情况:
- 纯腹部放射是诊断新生儿死性肠球炎 (NEC) 的标准.
- 由于微妙的放射特征,早期NEC诊断具有挑战性.
- 深度学习 (DL) 提供了提高诊断准确性的潜力.
研究的目的:
- 评估DL模型在I期NEC早期诊断中的有效性,使用普通腹部X射线.
- 将DL模型的诊断性能与人类专家进行比较.
主要方法:
- 在两个中心对680名新生儿进行了回顾性分析 (2016年6月 - 2023年12月).
- 使用DenseNet121提取的DL特征;用后勤回归 (LR) 和随机森林 (RF) 构建的放射学模型.
- 在培训和外部验证队伍中通过ROC曲线评估绩效;与人类专家绩效直接比较.
主要成果:
- 在训练 (LR AUC: 0.972,RF AUC: 0.961) 和验证 (LR AUC: 0.964,RF AUC: 0.951) 队列中,DL模型实现了高精度.
- 在诊断I阶段NEC时,DL模型显著超过了人类专家.
- 25个DL特征被确定为模型开发的关键.
结论:
- 使用普通腹部X射线图的DL模型对于在新生儿中识别I期NEC是有效的.
- 这种非侵入性方法可以提高早期NEC诊断,并支持临床决策.
- 人工智能驱动的成像分析显示了改善新生儿护理结果的前景.
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