人工智能支持胎儿心声学与质量评估
Caroline A Taksoee-Vester1,2,3, Kamil Mikolaj4, Zahra Bashir5,6,7
1Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. ca_tv@hotmail.com.
Scientific reports
|March 9, 2024
概括
这项研究开发了一种深度学习模型,用于胎儿心声学质量评估. 现实世界的数据对于AI模型开发至关重要,因为图像质量低会影响性能和临床一致性.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 心脏病学 心脏病学
背景情况:
- 胎儿心声学质量评估对于准确诊断至关重要.
- 深度学习模型显示出希望,但需要在不同的数据集上进行验证.
- 现实世界的数据变化可能会影响人工智能模型的性能.
研究的目的:
- 开发和未来验证一个深度学习模型来评估胎儿心声学质量.
- 评估图像质量对模型性能和临床协议的影响.
- 强调在医学成像中使用真实世界的数据来开发AI模型的重要性.
主要方法:
- 在5351个怀孕 (2008-2018) 的5363张胎儿心声图像上训练了一种深度学习模型.
- 在100名患者的192张图像上进行了前性验证.
- 使用细分和质量评分 (QS) 评估模型准确性.
- 对照临床医生和专家关于图像质量和细分的协议.
主要成果:
- 该模型的平均精度为0.91 (SD 0.09),图像的精度更高 (0.97,SD 0.03),QS高于平均水平.
- 临床医生认为44.8%的潜在验证图像与手动捕获相等,偏好自动捕获在18.69%和手动捕获在36.51%.
- 较高的QS与专家之间更好的对细分和QS的共识相关 (p < 0.001).
- 低QS负面影响了模型性能和临床医生的同意.
结论:
- 对于胎儿心声学质量评估的深度学习模型是可行的.
- 图像质量显著影响AI模型性能和临床实用性.
- 在现实世界中开发和验证人工智能模型",噪音"数据对于临床翻译至关重要.
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