延时培养联合人工智能能否提高卵裂期胚胎新鲜周期移植的临床妊娠率？
Can time-lapse culture combined with artificial intelligence improve ongoing pregnancy rates in fresh transfer cycles of single cleavage stage embryos?
机构信息
Reproductive Medicine Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China.
Department of Reproductive Medicine Center, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China.
出版信息
Front Endocrinol (Lausanne). 2024 Aug 29;15:1449035. doi: 10.3389/fendo.2024.1449035. eCollection 2024.
PURPOSE
With the rapid advancement of time-lapse culture and artificial intelligence (AI) technologies for embryo screening, pregnancy rates in assisted reproductive technology (ART) have significantly improved. However, clinical pregnancy rates in fresh cycles remain dependent on the number and type of embryos transferred. The selection of embryos with the highest implantation potential is critical for embryologists and influences transfer strategies in fertility centers. The superiority of AI over traditional morphological scoring for ranking cleavage-stage embryos based on their implantation potential remains controversial.
METHODS
This retrospective study analyzed 105 fresh embryo transfer cycles at the Centre for Reproductive Medicine from August 2023 to March 2024, following IVF/ICSI treatment at the cleavage stage. All embryos were cultured using time-lapse technology and scored using an automated AI model (iDAScore V2.0). Embryos were categorized into three groups based on the iDAScore V2.0: Group A (8 cells, iDA: 1.0-5.7); Group B (8 cells, iDA: 5.8-8.0); and Group C (>8 cells, iDA: 5.8-8.0). Clinical treatment outcomes, embryonic development, and pregnancy outcomes were analyzed and compared across the groups.
RESULTS
Baseline characteristics such as patient age, AMH levels, AFC, and basal sex hormones showed no significant differences among the three groups (p > 0.05). The iDAscores were significantly higher in Group C (7.3 ± 0.5) compared to Group B (6.7 ± 0.5) and the iDAscores were significantly higher in Group B (6.7 ± 0.5) compared to Group A (4.8 ± 1.0) (p < 0.001).The mean number of high-quality embryos was highest in Group C (4.7 ± 3.0), followed by Group B (3.6 ± 1.7) and Group A (2.1 ± 1.2) (p < 0.001). There was no statistical difference (p = 0.392) in the ongoing pregnancy rate for single cleavage-stage transfers between Group B (54.5%, 30/55) and Group A (38.1%, 8/21), although there was a tendency for Group B to be higher.
CONCLUSION
Combining time-lapse culture with AI scoring may enhance ongoing pregnancy rates in single cleavage-stage fresh transfer cycles.
目的
随着时间推移培养和胚胎筛选人工智能（AI）技术的快速发展，辅助生殖技术（ART）中的妊娠率有了显著提高。然而，新鲜周期的临床妊娠率仍然取决于移植胚胎的数量和类型。选择具有最高着床潜力的胚胎对于胚胎学家来说至关重要，并且影响生育中心的转移策略。AI 在基于着床潜力对卵裂期胚胎进行分级方面优于传统形态学评分的优越性仍存在争议。
方法
本回顾性研究分析了 2023 年 8 月至 2024 年 3 月在生殖医学中心进行的 105 例新鲜胚胎转移周期，这些周期均接受了体外受精/卵胞浆内单精子注射（ICSI）治疗。所有胚胎均使用时间推移技术进行培养，并使用自动化 AI 模型（iDAScore V2.0）进行评分。根据 iDAScore V2.0 将胚胎分为三组：A 组（8 细胞，iDA：1.0-5.7）；B 组（8 细胞，iDA：5.8-8.0）；C 组（>8 细胞，iDA：5.8-8.0）。分析比较了各组的临床治疗结局、胚胎发育和妊娠结局。
结果
三组患者的年龄、抗苗勒管激素（AMH）水平、基础窦卵泡数（AFC）和基础性激素等基线特征无显著差异（p>0.05）。C 组（7.3±0.5）的 iDA 评分明显高于 B 组（6.7±0.5），B 组的 iDA 评分明显高于 A 组（4.8±1.0）（p<0.001）。C 组的优质胚胎数量最高（4.7±3.0），其次是 B 组（3.6±1.7）和 A 组（2.1±1.2）（p<0.001）。B 组（54.5%，30/55）和 A 组（38.1%，8/21）的单卵裂期转移的持续妊娠率无统计学差异（p=0.392），尽管 B 组的持续妊娠率有上升趋势。
结论
将时间推移培养与 AI 评分相结合，可能会提高单卵裂期新鲜转移周期的持续妊娠率。
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