开发和验证一种基于人工智能的查方法,用于儿童胸部X射线中的心房隔膜缺陷
Li Zhixin1, Luo Gang1, Ji Zhixian1
1Heart Center, Women and Children's Hospital, Qingdao University, Qingdao, China.
Frontiers in pediatrics
|September 27, 2023
概括
应用到胸部X射线 (CXR) 的深度学习模型可以准确地检测儿童的心房隔膜缺陷. 这种人工智能方法达到90%以上的准确性,提供了一个有前途的选工具.
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
- 儿科心脏病学 儿科心脏病学
背景情况:
- 早期发现心房隔膜缺陷 (ASD) 对于儿童的有效治疗至关重要.
- 胸部X射线 (CXR) 是一种传统的,具有成本效益的成像方式.
研究的目的:
- 开发和验证使用CXR检测ASD的客观深度学习 (DL) 方法.
- 建立一个人工智能驱动的儿童自闭症查工具.
主要方法:
- 对420次心声回声和CXR (2018-2022) 的回顾性分析.
- 人工智能模型在经验丰富的心脏病学家分类的CXR数据上进行训练和验证.
- 使用AUC,灵敏度,特异性,精度,PPV和NPV进行性能评估.
主要成果:
- 开发的DL模型显示了高选准确度和超过90%的召回率.
- 人工智能模型在验证数据集上实现了显著的性能指标.
结论:
- 使用CXR射线图的深度学习模型为ASD评估提供了一个非常有利的方法.
- 这种人工智能方法为儿科心房隔膜缺陷查提供了可行的和高效的工具.
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