深度学习模型用于在机械通风的新生儿中早期和准确诊断与呼吸机相关的肺炎
Jen-Fu Hsu1, Ying-Chih Lin2, Chun-Yuan Lin3
1Division of Pediatric Neonatology, Department of Pediatrics, Chang Gung Memorial Hospital, Taoyuan, Taiwan; College of Medicine, Chang Gung University, Taoyuan, Taiwan.
Computers in biology and medicine
|March 4, 2025
概括
深度学习模型在使用胸部X射线诊断新生儿的呼吸机相关肺炎 (VAP) 中表现有前途. 这种人工智能辅助的方法可以提高诊断准确度,并指导婴儿重症监护室的抗生素治疗决策.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 新生儿医学 新生儿医学
背景情况:
- 呼吸机相关肺炎 (VAP) 需要在新生儿中进行早期和准确的诊断,以优化治疗并减少抗生素的使用.
- 深度学习 (DL) 方法正在探索,以帮助在新生儿重症监护室 (NICU) 内的直管新生儿中进行VAP诊断.
研究的目的:
- 评估深度学习 (DL) 模型在帮助诊断呼吸机相关性肺炎 (VAP) 的有效性.
- 用胸部X射线图像来比较各种DL方法的诊断性能.
主要方法:
- 一项前性研究观察了从2017年10月到2022年3月的670名新生儿在第三级NICU中接受机械通风.
- 收集了胸部X射线图像,并应用DL模型来预测VAP诊断,比较准确度,精度,灵敏度,特异性,F1得分和AUCs.
主要成果:
- 一个组合DL模型 (ResNet50和RegNetX80) 在机械通风的新生儿中诊断VAP的AUC达到0.8023.
- RegNetX80表现出高特异性 (0.8378),最大限度地减少了假阳性.
- 可解释的人工智能 (XAI) 提供了一致的反,增加了临床医生的对人工智能辅助诊断的信心.
结论:
- 深度学习模型通过胸部X射线证明了在新生儿中诊断VAP的适用性和良好的预测准确性.
- 人工智能辅助诊断可以支持临床决策,针对重症新生儿的实证抗生素选择.
- 需要进一步的前性试验来确认临床效用和节约资源的好处.
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