使用人工智能的超声波对低可塑性左心综合征的产前诊断:性能与当前查计划相比如何?
Thomas G Day1,2, Samuel Budd2, Jeremy Tan2
1Department of Congenital Heart Disease, Evelina Children's Healthcare, Guy's and St Thomas' NHS Foundation Trust, London, UK.
Prenatal diagnosis
|September 30, 2023
概括
人工智能 (AI) 显示出对先天性心脏病的产前查的潜力. 然而,当前用于检测低可塑性左心综合征 (HLHS) 的AI模型的性能略低于现有的国家查计划.
科学领域:
- 医疗成像医学成像
- 医疗保健中的人工智能
- 胎儿心脏病学 胎儿心脏病学
背景情况:
- 先天性心脏病在产前诊断中是一个重大挑战.
- 缺血性左心综合征 (HLHS) 是一种严重的先天性心脏缺陷,需要早期检测.
- 目前的国家查计划在检测HLHS方面存在局限性.
研究的目的:
- 评估人工智能 (AI) 模型在检测低可塑性左心综合征 (HLHS) 的性能.
- 为了比较人工智能模型的表现与目前的国家产前查计划HLHS.
主要方法:
- 对目前国家查计划数据进行分析,以检测HLHS.
- 使用胎儿心脏四室超声波视图和ResNet分类器开发和训练AI模型.
- 对人工智能模型 (灵敏度和特异性) 的比较性能分析与已建立的选计划.
主要成果:
- 目前的查计划显示HLHS的高灵敏度 (94.3%) 和特异性 (99.985%).
- 人工智能模型实现了高灵敏度 (100%) 与较低的特异性 (94.0%) 或高特异性 (100%) 与稍低的灵敏度 (93.3%).
- 一个高度敏感的AI模型产生了更多的假阳性,而一个高度特定的模型错过了更多的HLHS病例与当前程序相比.
结论:
- 独立地,开发的AI模型在HLHS检测中的性能与现有的查计划相比,目前还不够理想.
- 未来使用人工智能模型可能会导致性能进一步下降.
- 人类-人工智能合作对于人工智能在HLHS产前查中的有效临床实施至关重要.
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