Shri Ramdeobaba College of Engineering and Management, Ramdeobaba University, Nagpur, 440013, India.
Visvesvaraya National Institute of Technology, Computer Science and Engineering, Nagpur, 440010, India.
Sci Rep. 2024 Nov 14;14(1):28019. doi: 10.1038/s41598-024-79175-8.
Efficient prediction of blastocyst formation from early-stage human embryos is imperative for improving the success rates of assisted reproductive technology (ART). Clinics transfer embryos at the blastocyst stage on Day-5 but Day-3 embryo transfer offers the advantage of a shorter culture duration, which reduces exposure to laboratory conditions, potentially enhancing embryonic development within a more conducive uterine environment and improving the likelihood of successful pregnancies. In this paper, we present a novel ResNet-GRU deep-learning model to predict blastocyst formation at 72 HPI. The model considers the time-lapse images from the incubator from Day 0 to Day 3. The model predicts blastocyst formation with a validation accuracy of 93% from the cleavage stage. The sensitivity and specificity are 0.97 and 0.77 respectively. The deep learning model presented in this paper will assist the embryologist in identifying the best embryo to transfer at Day 3, leading to improved patient outcomes and pregnancy rates in ART.
从人类早期胚胎中高效预测囊胚形成对于提高辅助生殖技术（ART）的成功率至关重要。临床医生在第 5 天进行囊胚期胚胎移植，但第 3 天胚胎移植具有更短的培养时间优势，这减少了胚胎暴露在实验室条件下的时间，潜在地促进了更有利于子宫内环境的胚胎发育，并提高了成功妊娠的可能性。在本文中，我们提出了一种新的 ResNet-GRU 深度学习模型，用于预测 72 小时培养时的囊胚形成。该模型考虑了从第 0 天到第 3 天培养箱中的延时图像。该模型在卵裂期预测囊胚形成的验证准确率为 93%。灵敏度和特异性分别为 0.97 和 0.77。本文提出的深度学习模型将帮助胚胎学家在第 3 天识别出最佳的移植胚胎，从而提高 ART 中的患者结局和妊娠率。
