优化辅助生殖技术程序的人工智能算法:系统综述

F. Bulletti, Marco Berrettini, R. Sciorio, C. Bulletti
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引用次数: 0

摘要

人工智能(AI)近年来发展迅速,许多应用正在提高整个辅助生殖技术(ART)过程的单步效率。在这篇综述中,我们收集了所有提供ART的算法,并选择了那些支持临床辅助的算法,直到成功尝试。进一步选择那些在改善ART表现方面有明确作用的项目。我们发现了一种基于问卷的算法,可以识别有子宫内膜异位症风险的患者,并进行早期治疗和更好的生育结果。该算法可以根据逐步的规模分配来检测简单配子产量(雄性)和储备配子产量(雌性)的值,并显示正常或异常、自发或受刺激配子产量的主题。这可以为正在进行诊断和治疗的不孕夫妇提供显著的好处。促性腺激素起始剂量的计算器和控制卵巢刺激时的触发时间使临床管理更有效。随着人工智能在ART中的应用,确定囊胚形成所需的中期II期卵母细胞的最佳数量和胚胎生成所需的卵母细胞数量的能力得到了显著提高。在不同的计算器中,使用子宫内膜血管形成的超声或移植胚胎的年龄和整倍体来计算着床率,可能会进一步提高ART手术的管理水平,让更多的夫妇参与其中,提高手术的疗效。最后,基于夫妇或医疗中心分析和效率的ART计划的推定成功计算器对夫妇来说是巨大的安慰。总之,算法和机器学习在人类生殖领域的发展日新月异,带来了明显的好处。通过体外受精(IVF)治疗不孕症有几种算法辅助,这些算法提高了每个程序步骤的效率,使体外受精程序的管理更加轻松。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial intelligence algorithms for optimizing assisted reproductive technology programs: A systematic review
Artificial intelligence (AI) has been experiencing rapid growth in recent years, and numerous applications are improving the single-step efficiency of the whole assisted reproductive technology (ART) procedure. In this review, we collected all the algorithms supplying ART and selected those supporting the clinical assistance to the procedure up to the successful attempt. Those with a clear role in improving ART performances were further selected. We found a questionnaire-based algorithm identifying patients at risk for endometriosis with early management and better fertility outcome. An algorithm can detect the values of simple gamete production (male) and reservoir (female) according to gradual scale allocation, and display themas normal or abnormal, spontaneousor stimulated gamete production. This can provide significant benefits for infertile couples undergoing diagnostic and therapeutic journeys. The calculators for the starting dose of gonadotropins and the trigger timing during controlled ovarian stimulation make clinical management more efficient. With the application of AI in ART, the ability to determine the optimal number of metaphase II oocytes required for blastocyst formation and number of oocytes needed for embryo production has been significantly improved. The calculation of the implantation rate as proposed in different calculators, using the ultrasound of endometrial vascularization or the age and euploidy of the embryo transferred, may provide further advancement in managing the ART procedure with more participation from the couples to increase the efficacy of the procedures. Finally, the calculator of presumptive success with an ART program based on couples or medical center profiling and efficiency is of tremendous comfort to couples. In conclusion, algorithms and machine learning development in human reproduction are growing daily with evident benefits. Infertility treatments by in vitro fertilization (IVF) are assisted by several algorithms that improve the efficiency of each procedure step, making IVF program’s management more effortless.
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