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Deep Learning in IVF to Predict the Embryo Infertility from Blastocyst Images

机译:IVF中的深度学习预测胚泡图像的胚胎不育症

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In Vitro Fertilization (IVF) is used to solve infertility problem caused due to damaged, blocked, weak, total absence of fallopian tubes and issues in sperm or endometriosis. Successful IVF depends on assessment of embryo quality. In visual morphology, assessment produced by embryologists are different, as an outcome low success rate of IVF is seen. To develop the success rate multiple embryos are planted which lead to several pregnancies and complications. Artificial Intelligence (AI) method can be followed to analyze embryo quality apart from human involvement. Deep learning model is proposed to analyze human blastocyst quality and to achieve 85% of test accuracy.
机译:体外施肥(IVF)用于解决由于受损,障碍,弱,缺乏输卵管和精子或子宫内膜异位症的问题而导致的不孕症问题。成功的IVF取决于对胚胎质量的评估。在视觉形态中,胚胎生理学家产生的评估是不同的,因为观察到IVF的结果低成功率。发展成功率,种植多重胚胎,导致几种怀孕和并发症。可以遵循人工智能(AI)方法,以分析胚胎质量与人类参与之外。建议深入学习模型分析人胚性质量,达到85%的测试精度。

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