Time-lapse videography for embryo selection/de-selection: a bright future or fading star?

Liu, Yanhe; Sakkas, Denny; Afnan, Masoud; Matson, Phillip

doi:10.1080/14647273.2019.1598586

Cited by 8 publications

(7 citation statements)

References 62 publications

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“…It has been recently proposed that qualitative parameters may improve reproducibility of embryo deselection between different laboratories [40]. Binary outcomes of qualitative parameters are independent of absolute cleavage timings of embryos.…”

Section: Discussionmentioning

confidence: 99%

Between-laboratory reproducibility of time-lapse embryo selection using qualitative and quantitative parameters: a systematic review and meta-analysis

Liu

Qi²,

Matson

et al. 2020

J Assist Reprod Genet

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Purpose To investigate the between-laboratory reproducibility of embryo selection/deselection effectiveness using qualitative and quantitative time-lapse parameters. Methods A systematic search was performed on MEDLINE, EMBASE, and the Cochrane Library (up to February 2020) without restriction on date, language, document type, and publication status. Measuring outcomes included implantation, blastulation, good-quality blastocyst formation, and euploid blastocyst. Results We detected 6 retrospective cohort studies externally validating the first clinical time-lapse model (Meseguer) emphasizing quantitative parameters, of which 3 (including one involving 2 independent centers) were included for the pooled analysis. Receiver operating characteristics analysis showed reduced predictive power of the model when either including or not including sister clinic validation. Fifteen cohort studies evaluating qualitative parameters were included for meta-analysis, and the mean Newcastle-Ottawa Scale was 5.3. Overall, meta-analysis showed significantly adverse association between the presence of ≥ 1 cleavage abnormalities and embryo implantation rates (11 studies, n = 7266; RR = 0.39[0.28, 0.55] 95% CI ; I 2 = 57%). Further analysis showed adverse impacts of direct cleavage (7 studies, n = 7065; RR = 0.28 [0.15, 0.54] 95% CI ; I 2 = 46%), reverse cleavage (2 studies, n = 3622; RR = 0.16 [0.03, 0.75] 95% CI ; I 2 = 0%), chaotic cleavage (2 studies, n = 3643; RR = 0.11 [0.02, 0.69] 95% CI ; I 2 = 24%), and multinucleation (5 studies, n = 2576; RR = 0.59 [0.50, 0.69] 95% CI ; I 2 = 0%), but not the < 6 intercellular contact points at the 4-cell stage (1 study, n = 185; RR = 0.17 [0.02, 1.15] 95% CI ). Conclusions Qualitative time-lapse parameters are reliably associated with embryo developmental potential among laboratories, whereas the reproducibility of time-lapse embryo selection model that emphasizes quantitative parameters may be compromised when externally applied.

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Section: Discussionmentioning

confidence: 99%

Between-laboratory reproducibility of time-lapse embryo selection using qualitative and quantitative parameters: a systematic review and meta-analysis

Liu

Qi²,

Matson

et al. 2020

J Assist Reprod Genet

Self Cite

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“…Although manual morphological annotation and quality assessment of embryos fertilized in vitro remains the gold standard for predicting IVF success, efforts to standardize and improve prediction accuracy have become increasingly computational (several reviews have been published discussing such approaches from various points of view (Simopoulou et al, 2018b,a;Del Gallego et al, 2019;Liu et al, 2019;Basile et al, 2015)). Most algorithms developed for embryo outcome prediction require user-defined input parameters (such as specific morphological characteristics), execute a series of user-defined tasks, and then produce an estimated probability of achieving a user-defined outcome.…”

Section: Introductionmentioning

confidence: 99%

“…Lack of standardization and agreement on criteria likely contribute to the low success rate of IVF. Researchers in the assisted reproductive technology (ART) community have, therefore, increasingly turned to machine learning techniques in recent years (Simopoulou et al, 2018b;Liu et al, 2019;Curchoe and Bormann, 2019;Wang et al, 2019;Zaninovic et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Prediction of Embryo Implantation Probability Using IVF Time-lapse Imaging

Silver,

Feder,

Gold-Zamir

et al. 2020

Preprint

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The process of fertilizing a human egg outside the body in order to help those suffering from infertility to conceive is known as in vitro fertilization (IVF). Despite being the most effective method of assisted reproductive technology (ART), the average success rate of IVF is a mere 20-40%. One step that is critical to the success of the procedure is selecting which embryo to transfer to the patient, a process typically conducted manually and without any universally accepted and standardized criteria. In this paper we describe a novel data-driven system trained to directly predict embryo implantation probability from embryogenesis time-lapse imaging videos. Using retrospectively collected videos from 272 embryos, we demonstrate that, when compared to an external panel of embryologists, our algorithm results in a 12% increase of positive predictive value and a 29% increase of negative predictive value.

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“…Yet, our understanding of the interplay between embryos' morphology, viability, and maternal age is limited, as manual approaches to infer embryo morphokinetics are timeconsuming, subjective, and prone to errors. Machine learning [15][16][17][18] was recently harnessed to predict embryo developmental potential 19,20 , however, with limited success.Here, we develop an artificial intelligence (AI) platform that infers the embryos' developmental stage and captures tens of morphological properties and developmental dynamics. We show that developmental timing is the most informative and predictive morphokinetic property, particularly for embryos from maternally aged females.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

Machine Learning Reveals The Effect of Maternal Age on The Mouse Pre-Implantation Embryo Developmental Timing

Daniel

Wasserman

Adler

et al. 2022

Preprint

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In recent years, many women have delayed childbearing, thus increasing the necessity for assisted reproductive technology (ART) for older women. Despite advances in ART, its success rate in advanced-age women is still very low. As time-lapse imaging became available, morphological features of the developing pre-implantation embryo, in-vitro, are heavily used to assess its potency. Timing of embryo cleavage is also an important factor that correlates with blastocyst formation and pregnancy rates. Yet, our understanding of the interplay between embryos' morphology, viability, and maternal age is limited, as manual approaches to infer embryo morphokinetics are time-consuming, subjective, and prone to errors. Machine learning was recently harnessed to predict embryo developmental potential, however, with limited success. Here, we develop an artificial intelligence (AI) platform that infers the embryos' developmental stage and captures tens of morphological properties and developmental dynamics. We show that developmental timing is the most informative and predictive morphokinetic property, particularly for embryos from maternally aged females. Analyzing the timing distributions reveals that viable embryos are confined into an age-independent temporal corridor while non-viable embryos deviate from it towards slower transition times. Yet, the deviation of non-viable embryos from the temporal corridor is age-dependent. Furthermore, there is a significant correlation between consecutive developmental stages transition times that diminishes in maternally old embryos. Overall, our results suggest that maternally old embryos' most apparent morphokinetic property is the loss of temporal regulation. Our results and platform pave the way for a more accurate, maternally-age-dependent, assisted reproductive technology.

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Time-lapse videography for embryo selection/de-selection: a bright future or fading star?

Cited by 8 publications

References 62 publications

Between-laboratory reproducibility of time-lapse embryo selection using qualitative and quantitative parameters: a systematic review and meta-analysis

Between-laboratory reproducibility of time-lapse embryo selection using qualitative and quantitative parameters: a systematic review and meta-analysis

Data-Driven Prediction of Embryo Implantation Probability Using IVF Time-lapse Imaging

Machine Learning Reveals The Effect of Maternal Age on The Mouse Pre-Implantation Embryo Developmental Timing

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