PDF(Pubmed)
Abstract：
BACKGROUND: Several studies have demonstrated that iDAScore is more accurate in predicting pregnancy outcomes in cycles without preimplantation genetic testing for aneuploidy (PGT-A) compared to KIDScore and the Gardner criteria. However, the effectiveness of iDAScore in cycles with PGT-A has not been thoroughly investigated. Therefore, this study aims to assess the association between artificial intelligence (AI)-based iDAScore (version 1.0) and pregnancy outcomes in single-embryo transfer (SET) cycles with PGT-A.
METHODS: This retrospective study was approved by the Institutional Review Board of Chung Sun Medical University, Taichung, Taiwan. Patients undergoing SET cycles (n = 482) following PGT-A at a single reproductive center between January 2017 and June 2021. The blastocyst morphology and morphokinetics of all embryos were evaluated using a time-lapse system. The blastocysts were ranked based on the scores generated by iDAScore, which were defined as AI scores, or by KIDScore D5 (version 3.2) following the manufacturer\'s protocols. A single blastocyst without aneuploidy was transferred after examining the embryonic ploidy status using a next-generation sequencing-based PGT-A platform. Logistic regression analysis with generalized estimating equations was conducted to assess whether AI scores are associated with the probability of live birth (LB) while considering confounding factors.
RESULTS: Logistic regression analysis revealed that AI score was significantly associated with LB probability (adjusted odds ratio [OR] = 2.037, 95% confidence interval [CI]: 1.632-2.542) when pulsatility index (PI) level and types of chromosomal abnormalities were controlled. Blastocysts were divided into quartiles in accordance with their AI score (group 1: 3.0-7.8; group 2: 7.9-8.6; group 3: 8.7-8.9; and group 4: 9.0-9.5). Group 1 had a lower LB rate (34.6% vs. 59.8-72.3%) and a higher rate of pregnancy loss (26% vs. 4.7-8.9%) compared with the other groups (p < 0.05). The receiver operating characteristic curve analysis verified that the iDAScore had a significant but limited ability to predict LB (area under the curve [AUC] = 0.64); this ability was significantly weaker than that of the combination of iDAScore, type of chromosomal abnormalities, and PI level (AUC = 0.67). In the comparison of the LB groups with the non-LB groups, the AI scores were significantly lower in the non-LB groups, both for euploid (median: 8.6 vs. 8.8) and mosaic (median: 8.0 vs. 8.6) SETs.
CONCLUSIONS: Although its predictive ability can be further enhanced, the AI score was significantly associated with LB probability in SET cycles. Euploid or mosaic blastocysts with low AI scores (≤ 7.8) were associated with a lower LB rate, indicating the potential of this annotation-free AI system as a decision-support tool for deselecting embryos with poor pregnancy outcomes following PGT-A.
METHODS: This retrospective study was approved by the Institutional Review Board of Chung Sun Medical University, Taichung, Taiwan. Patients undergoing SET cycles (n = 482) following PGT-A at a single reproductive center between January 2017 and June 2021. The blastocyst morphology and morphokinetics of all embryos were evaluated using a time-lapse system. The blastocysts were ranked based on the scores generated by iDAScore, which were defined as AI scores, or by KIDScore D5 (version 3.2) following the manufacturer\'s protocols. A single blastocyst without aneuploidy was transferred after examining the embryonic ploidy status using a next-generation sequencing-based PGT-A platform. Logistic regression analysis with generalized estimating equations was conducted to assess whether AI scores are associated with the probability of live birth (LB) while considering confounding factors.
RESULTS: Logistic regression analysis revealed that AI score was significantly associated with LB probability (adjusted odds ratio [OR] = 2.037, 95% confidence interval [CI]: 1.632-2.542) when pulsatility index (PI) level and types of chromosomal abnormalities were controlled. Blastocysts were divided into quartiles in accordance with their AI score (group 1: 3.0-7.8; group 2: 7.9-8.6; group 3: 8.7-8.9; and group 4: 9.0-9.5). Group 1 had a lower LB rate (34.6% vs. 59.8-72.3%) and a higher rate of pregnancy loss (26% vs. 4.7-8.9%) compared with the other groups (p < 0.05). The receiver operating characteristic curve analysis verified that the iDAScore had a significant but limited ability to predict LB (area under the curve [AUC] = 0.64); this ability was significantly weaker than that of the combination of iDAScore, type of chromosomal abnormalities, and PI level (AUC = 0.67). In the comparison of the LB groups with the non-LB groups, the AI scores were significantly lower in the non-LB groups, both for euploid (median: 8.6 vs. 8.8) and mosaic (median: 8.0 vs. 8.6) SETs.
CONCLUSIONS: Although its predictive ability can be further enhanced, the AI score was significantly associated with LB probability in SET cycles. Euploid or mosaic blastocysts with low AI scores (≤ 7.8) were associated with a lower LB rate, indicating the potential of this annotation-free AI system as a decision-support tool for deselecting embryos with poor pregnancy outcomes following PGT-A.
摘要：
背景：多项研究表明，与KIDScore和Gardner标准相比，在不进行非整倍性植入前遗传学测试（PGT-A）的情况下，iDAScore在预测周期妊娠结局方面更为准确。然而,iDAScore在PGT-A治疗周期中的有效性尚未得到彻底研究.因此,这项研究旨在评估基于人工智能（AI）的iDAScore（版本1.0）与PGT-A的单胚胎移植（SET）周期中妊娠结局之间的关联。
方法：这项回顾性研究得到了重阳医科大学机构审查委员会的批准，台中,台湾。在2017年1月至2021年6月期间，在单个生殖中心接受PGT-A后进行SET周期（n=482）的患者。使用延时系统评估所有胚胎的胚泡形态和形态动力学。根据iDAScore产生的分数对胚泡进行排名，定义为AI分数，或通过KIDScoreD5(版本3.2)遵循制造商的协议。在使用基于下一代测序的PGT-A平台检查胚胎倍性状态后，转移了没有非整倍性的单个胚泡。使用广义估计方程进行Logistic回归分析，以评估AI得分是否与活产概率（LB）相关，同时考虑混杂因素。
结果：Logistic回归分析显示，当控制搏动指数（PI）水平和染色体异常类型时，AI得分与LB概率（调整后比值比[OR]=2.037，95％置信区间[CI]：1.632-2.542）显着相关。根据AI评分将囊胚分为四分位数（第1组：3.0-7.8；第2组：7.9-8.6；第3组：8.7-8.9；第4组：9.0-9.5）。第1组的LB率较低（34.6%vs.59.8-72.3％）和更高的妊娠损失率（26％vs.4.7-8.9％）与其他组相比（p<0.05）。受试者工作特征曲线分析证实，iDAScore具有显著但有限的预测LB的能力（曲线下面积[AUC]=0.64）；这种能力明显弱于iDAScore的组合，染色体异常的类型，和PI水平(AUC=0.67)。在LB组和非LB组的比较中，非LB组的AI得分明显较低，均为整倍体（中位数：8.6vs.8.8）和马赛克（中位数：8.0vs.8.6)SET。
结论：虽然其预测能力可以进一步提高，在SET周期中,AI评分与LB概率显著相关.低AI评分（≤7.8）的Euploid或镶嵌胚泡与较低的LB率相关，表明这种无注释的AI系统作为决策支持工具的潜力，用于取消选择PGT-A后妊娠结局不良的胚胎。
方法：这项回顾性研究得到了重阳医科大学机构审查委员会的批准，台中,台湾。在2017年1月至2021年6月期间，在单个生殖中心接受PGT-A后进行SET周期（n=482）的患者。使用延时系统评估所有胚胎的胚泡形态和形态动力学。根据iDAScore产生的分数对胚泡进行排名，定义为AI分数，或通过KIDScoreD5(版本3.2)遵循制造商的协议。在使用基于下一代测序的PGT-A平台检查胚胎倍性状态后，转移了没有非整倍性的单个胚泡。使用广义估计方程进行Logistic回归分析，以评估AI得分是否与活产概率（LB）相关，同时考虑混杂因素。
结果：Logistic回归分析显示，当控制搏动指数（PI）水平和染色体异常类型时，AI得分与LB概率（调整后比值比[OR]=2.037，95％置信区间[CI]：1.632-2.542）显着相关。根据AI评分将囊胚分为四分位数（第1组：3.0-7.8；第2组：7.9-8.6；第3组：8.7-8.9；第4组：9.0-9.5）。第1组的LB率较低（34.6%vs.59.8-72.3％）和更高的妊娠损失率（26％vs.4.7-8.9％）与其他组相比（p<0.05）。受试者工作特征曲线分析证实，iDAScore具有显著但有限的预测LB的能力（曲线下面积[AUC]=0.64）；这种能力明显弱于iDAScore的组合，染色体异常的类型，和PI水平(AUC=0.67)。在LB组和非LB组的比较中，非LB组的AI得分明显较低，均为整倍体（中位数：8.6vs.8.8）和马赛克（中位数：8.0vs.8.6)SET。
结论：虽然其预测能力可以进一步提高，在SET周期中,AI评分与LB概率显著相关.低AI评分（≤7.8）的Euploid或镶嵌胚泡与较低的LB率相关，表明这种无注释的AI系统作为决策支持工具的潜力，用于取消选择PGT-A后妊娠结局不良的胚胎。
