对于产前先天性心脏病 (CHD) 查的深度学习模型一般化到社区环境中,并且优于临床检测
medRxiv : the preprint server for health sciences
|June 21, 2024
概括
深度学习 (DL) 模型在胎儿超声波中显著改善了先天性心脏缺陷 (CHD) 的检测,超过了人类专家. 这种人工智能工具有望提高产前查的准确性,即使是新的心脏病变.
科学领域:
- 医学成像分析分析 医学成像分析
- 医疗保健中的人工智能
- 胎儿医学 胎儿医学
背景情况:
- 在产前超声波查期间,先天性心脏缺陷 (CHD) 经常被遗漏,导致不良结果.
- 深度学习 (DL) 为自动化和提高医学图像解释的准确性提供了潜力.
研究的目的:
- 评估预先训练的DL模型在低风险人群的胎儿超声波图像上的表现.
- 将DL模型的诊断准确性与初步临床评估和盲人人类专家进行比较.
主要方法:
- 荷兰2015-2016年胎儿超声波图像的回顾性分析.
- 包括42个正常胎儿和66个孤立心血管疾病的病例.
- 临床诊断,DL模型性能和盲目的专家性能的比较.
主要成果:
- 临床诊断错过了53%的CHD病例;DL模型实现了91%的灵敏度和93%的特异性.
- 盲人人类专家获得了55%的灵敏度和71%的特异性.
- DL模型在图像上表现出强的性能,其训练数据中以前没有看到的病变.
结论:
- 在这个队列中,DL算法在通过胎儿超声波检测心血管疾病方面显著超过了人类专家.
- DL模型在社区获取的图像和新病变上显示出有效性,这表明其广泛适用性.
- DL模型有可能提高先天性心脏缺陷的产前检测率.
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