Application of Time-Lapse Imaging Technology and Artificial Intelligence in ART
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摘要: 准确评估胚胎发育对于辅助生殖技术（ART）成功至关重要。传统方法多基于主观的胚胎形态学评估，缺乏客观性和实时性。时差成像技术（TLT）提供更稳定的培养环境，实现胚胎发育的动态监测，分析和建模不同发育时期的动态学参数，用于预测胚胎的植入潜力。然而，动态学参数通常需要人工标注，引入主观干扰，数据模型分析能力差异较大，与实际情况相差甚远，尤其在染色体整倍性分析方面表现较弱。随着人工智能（AI）的不断发展，TLT与AI的结合提供了减少TLT人工标注时间、提高胚胎植入率和染色体整倍性预测等方面的可能性。旨在探讨TLT结合AI在形态学和动态学参数方面对胚胎植入潜力和染色体整倍性的应用。Abstract: Accurate assessment of embryo development is of paramount importance for the success of Assisted Reproductive Technologies （ART）. Traditional methods predominantly rely on subjective embryo morphological evaluations, lacking objectivity and real-time capabilities. Time-lapse Technology （TLT） offers a more stable incubation environment, facilitating dynamic monitoring of embryo development, analysis, and modeling of dynamic parameters during different developmental stages for predicting embryo implantation potential. However, dynamic parameters often require manual annotations, introducing subjective bias and exhibiting significant disparities in data modeling capabilities, deviating from real-world scenarios, especially in the analysis of chromosomal ploidy. With the continuous advancement of Artificial Intelligence （AI）, the integration of TLT and AI holds promise in reducing manual annotation time in TLT, enhancing embryo implantation rates, and chromosomal ploidy prediction. This review aims to explore the application of TLT combined with AI in morphological and dynamic parameters for assessing embryo implantation potential and chromosomal ploidy.
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表 1 TLT在预测胚胎妊娠结局上的应用
Table 1. Application of TLT in predicting embryo pregnancy outcomes
结论 参考文献 异常的卵裂模式不利于胚胎的着床 Liu等[13，15]、Rubio等[14]、 基于KIDscoreTM D5算法评分的胚胎选择，可以提高治疗的单胚胎种植率 Gazzo等[4] CPap、PNbd、 t2和S2可以预测质量较高的胚胎 Macedo等[16] GV到MI的持续时间与形成高质量胚胎之间具有相关性。 Yang等[17] TLT可以提高临床妊娠率 Guo等[18] TLT可以提高种植率 Kovacs等[5] TLT可以减少早期妊娠丢失 Rubio 等[19] 研究发现TLT组的活产率、种植率和临床妊娠率均显著高于常规组 Chera-Aree等[20] 认为时差成像技术的应用不能改善临床结果。 Kieslinger等[21] TLT能够选择移植的胚胎 Iwasawa等[22] TLT在选择单胚移植时可以维持妊娠率 Haikin Herzberger等[23] TLT虽可改善妊娠结局，但其临床妊娠率仍低于囊胚期的单胚移植，且早期流产率没有
显著性差异。田可可等[24] 
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表 2 TLT在预测胚胎染色体整倍性中的应用
Table 2. Application of TLT in predicting embryo chromosomal ploidy
结论 参考文献 染色体整倍性正常和异常胚胎在t5、cc2、cc3和t5-t2等参数上存在显著性差异。 Basile等[8]、Chawla等[9] 发现tPNf在染色体整倍性正常和异常胚胎之间也存在显著性差异。 Chawla等[9] tSC和tB在整倍性和非整倍性胚胎中存在显著性差异。 Campbell等[10] 发现囊胚扩张的动态学参数与胚胎的整倍性有相关性。 Huang等[11] 16个动态学参数与胚胎染色体非整倍体率之间没有相关性，可能与纳入患者的治疗类型和
患者的基础情况等多种因素有关。Rienzi等[29] KS5评分系统的评分与胚胎染色体整倍体率成正相关，还提高了单个整倍体囊胚移植的
种植率。Gazzo等[4]
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表 3 TLT和AI结合在预测胚胎种植潜能中的应用
Table 3. Application of TLT and AI combined in predicting embryo implantation potential
AI模型 结论 参考文献 深度学习模型 可以预测胚胎质量。 Jørgen Berntsen等[40]、Lee 等[46] 可以预测活产率。 Huang 等[30] 可以预测胚胎的染色体整倍性。 Lee 等[42] 可以对胚胎进行形态分级和选择。 Leahy等[49]、Thirumalaraju等[50]、Hammer等[51]、Danardono等[52]、Kanakasabapathy等[53]、Ueno等[54]、UenoBormann 等[31] 利用TLT技术获取的胚胎发育视频，建立模型，其预测整倍体的准确度达到了 0.8205 Elena Paya等[44] 对大量的胚胎发育图像进行分析，可提高预测胚胎质量的准确性 Khosravi[47] 注意力分支网络 对胚胎发育进行自动化标注和标准化形态评估，并预测囊胚移植
活产率。Sawada 等[41] EPA 能很好的预测胚胎的染色体整倍性。 Huang 等[43] LSTM 可预测胚胎质量的准确性。 Liao 等[48] 遗传学AI模型 预测预测胚胎倍性。 Diakiw等[57] RFC模型 预测胚胎倍性。 De等[58] 没有明确模型 AI结合TLT对普遍认为无利用价值的第7天胚胎进行评估，为反复移植失败患者，提高了胚胎利用率。 Cimadomo 等[34] AI定量描述囊胚扩张成度，从而预测囊胚的植入率。 Lagalla等[37]
Huang等[38]数字化囊胚形态学的评分预测胚胎非整倍性。 Chen 等[39] 基于多中心临床数据的训练算法的预测能力更佳，可以显著提高治疗临床结局的预测能力。 Duval 等[55] 在1项基于KID评分进行建模的多中心研究中，发现该数据模型在预测胚胎的种植潜能、囊胚发育潜能和囊胚质量方面有较好的
预测价值。Petersen等[56] 结合形态动态学特征、囊胚的形态学特征和患者的临床参数建立的模型，在预测整倍体方面表现出很好的效果。 Yuan等[32]
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