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Could metabolic imaging and artificial intelligence provide a novel path to non-invasive aneuploidy assessments? A certain clinical need.

作者信息

Horta Fabrizzio, Sakkas Denny, Ledger William, Goldys Ewa M, Gilchrist Robert B

机构信息

Fertility & Research Centre, Discipline of Women health, School of Clinical Medicine and the Royal Hospital for Women, University of New South Wales, Sydney, NSW, Australia; and Dept O&G, Monash University, Melbourne, Vic, Australia; and Monash Data Future Institute, Monash University, Melbourne, Vic, Australia; and City Fertility, Sydney, NSW, Australia.

Boston IVF, IVIRMA, Global Research Alliance, Waltham, MA, USA.

出版信息

Reprod Fertil Dev. 2025 Jan;37. doi: 10.1071/RD24122.

DOI:10.1071/RD24122
PMID:39874158
Abstract

Pre-implantation genetic testing for aneuploidy (PGT-A) via embryo biopsy helps in embryo selection by assessing embryo ploidy. However, clinical practice needs to consider the invasive nature of embryo biopsy, potential mosaicism, and inaccurate representation of the entire embryo. This creates a significant clinical need for improved diagnostic practices that do not harm embryos or raise treatment costs. Consequently, there has been an increasing focus on developing non-invasive technologies to enhance embryo selection. Such innovations include non-invasive PGT-A, artificial intelligence (AI) algorithms, and non-invasive metabolic imaging. The latter measures cellular metabolism through autofluorescence of metabolic cofactors. Notably, hyperspectral microscopy and fluorescence lifetime imaging microscopy (FLIM) have revealed unique metabolic activity signatures in aneuploid embryos and human fibroblasts. These methods have demonstrated high accuracy in distinguishing between euploid and aneuploid embryos. Thus, this review discusses the clinical challenges associated with PGT-A and emphasizes the need for novel solutions such as metabolic imaging. Additionally, it explores how aneuploidy affects cell behaviour and metabolism, offering an opinion perspective on future research directions in this field of research.

摘要

相似文献

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Could metabolic imaging and artificial intelligence provide a novel path to non-invasive aneuploidy assessments? A certain clinical need.
Reprod Fertil Dev. 2025 Jan;37. doi: 10.1071/RD24122.
2
Development of an artificial intelligence model for predicting the likelihood of human embryo euploidy based on blastocyst images from multiple imaging systems during IVF.基于体外受精过程中多个成像系统的囊胚图像,开发一种人工智能模型,用于预测人类胚胎整倍体的可能性。
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Non-invasive, label-free optical analysis to detect aneuploidy within the inner cell mass of the preimplantation embryo.应用非侵入性、无标记的光学分析技术,检测胚胎植入前内细胞团中的非整倍体。
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Fluorescence lifetime imaging microscopy (FLIM) detects differences in metabolic signatures between euploid and aneuploid human blastocysts.荧光寿命成像显微镜(FLIM)可检测整倍体和非整倍体人类囊胚之间代谢特征的差异。
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Minimizing mosaicism: assessing the impact of fertilization method on rate of mosaicism after next-generation sequencing (NGS) preimplantation genetic testing for aneuploidy (PGT-A).最大限度地减少嵌合体:评估胚胎植入前遗传学检测(PGT-A)中下一代测序(NGS)后不同受精方法对嵌合体发生率的影响。
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Implicit bias in diagnosing mosaicism amongst preimplantation genetic testing providers: results from a multicenter study of 36 395 blastocysts.胚胎植入前遗传学检测提供者在诊断嵌合体时的内隐偏倚:一项 36395 个囊胚的多中心研究结果。
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The use of copy number loads to designate mosaicism in blastocyst stage PGT-A cycles: fewer is better.在囊胚期植入前基因检测-非整倍体(PGT-A)周期中使用拷贝数负荷来确定嵌合现象:越少越好。
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Extended in vitro culture of human embryos demonstrates the complex nature of diagnosing chromosomal mosaicism from a single trophectoderm biopsy.人类胚胎的体外培养时间延长,表明从单个滋养外胚层活检中诊断染色体嵌合体的复杂性。
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Utilization of standardized preimplantation genetic testing for aneuploidy (PGT-A) via artificial intelligence (AI) technology is correlated with improved pregnancy outcomes in single thawed euploid embryo transfer (STEET) cycles.利用人工智能(AI)技术进行标准化的胚胎植入前遗传学检测(PGT-A)与单冻融整倍体胚胎移植(STEET)周期中妊娠结局的改善相关。
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Non-invasive preimplantation genetic testing for putative mosaic blastocysts: a pilot study.对可能存在嵌合体的囊胚进行非侵入性植入前遗传学检测:一项初步研究。
Hum Reprod. 2021 Jun 18;36(7):2020-2034. doi: 10.1093/humrep/deab080.

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