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用深度学习模型进行人类胚胎质量评估.

Maryam Kalatehjari1, Younes Ghasemi2, Shaghayegh Mahmoudiandehkordi3

  • 1Reproductive Sciences and Sexual Health Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.

Journal of obstetrics and gynaecology of India
|June 30, 2025
PubMed
概括
此摘要是机器生成的。

深度学习模型准确地评估了辅助生殖技术的胚胎质量. EfficientNetV2实现了95.26%的准确率,改善了生育治疗结果,并支持了未来的父母.

关键词:
在美国,CNN是CNN.深度学习是一种深度学习.预测胚胎生存能力的预测转移学习转移学习

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科学领域:

  • 辅助生殖技术 (ART) 是一种辅助生殖技术.
  • 医学成像分析 医学成像分析
  • 医疗保健中的人工智能

背景情况:

  • 胚胎质量评估对于成功的辅助生殖技术 (ART) 结果至关重要.
  • 胚胎学家对胚胎的主观视觉分类可能会导致不一致.
  • 深度学习为客观和可重复的胚胎评估提供了一条道路.

研究的目的:

  • 研究深度学习模型在分类胚胎质量的有效性.
  • 为了比较各种卷积神经网络 (CNN) 架构的性能.
  • 确定在第3天和第5天阶段准确评估胚胎的最佳模型.

主要方法:

  • 利用了来自Hung Vuong医院的胚胎图像数据集.
  • 培训和评估了四个CNN架构:VGG-19,ResNet-50,InceptionV3和EfficientNetV2.2,这些架构包括:
  • 使用准确性,精度和回忆指标评估模型性能.

主要成果:

  • 在测试的模型中,EfficientNetV2获得了最高的性能.
  • EfficientNetV2的准确率达到了95.26%,精度达到了96.30%,回忆率达到了97.25%.
  • 深度学习模型,特别是EfficientNetV2,显示出对胚胎质量评估的一致性的潜力.

结论:

  • EfficientNetV2是一个非常准确的工具,用于客观地评估胚胎质量.
  • 这种人工智能驱动的方法可以提高生育治疗的效率.
  • 客观的胚胎评估支持专家和未来的父母在生殖过程中.