Automatic time-lapse instrument is superior to single-point morphology observation for selecting viable embryos: retrospective study in oocyte donation

Aparicio-Ruiz, Belén; Basile, Natalia; Albalá, Sonia Pérez; Bronet, F.; Remohı́, José; Meseguer, Marcos

doi:10.1016/j.fertnstert.2016.07.1117

Cited by 49 publications

(32 citation statements)

References 27 publications

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“…Second, not all of them employ a clear-cut definition of embryonic asymmetry. Finally, the above-mentioned transient nature of the morphological characteristics, including asymmetry, would hinder their analysis [ 23 ].…”

Section: Discussionmentioning

confidence: 99%

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Inter-laboratory agreement on embryo classification and clinical decision: Conventional morphological assessment vs. time lapse

Martínez-Granados

Serrano²,

González-Útor³

et al. 2017

PLoS ONE

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The aim of this study is to determine inter-laboratory variability on embryo assessment using time-lapse platform and conventional morphological assessment. This study compares the data obtained from a pilot study of external quality control (EQC) of time lapse, performed in 2014, with the classical EQC of the Spanish Society for the Study of Reproductive Biology (ASEBIR) performed in 2013 and 2014. In total, 24 laboratories (8 using EmbryoScope™, 15 using Primo Vision™ and one with both platforms) took part in the pilot study. The clinics that used EmbryoScope™ analysed 31 embryos and those using Primo Vision™ analysed 35. The classical EQC was implemented by 39 clinics, based on an analysis of 25 embryos per year. Both groups were required to evaluate various qualitative morphological variables (cell fragmentation, the presence of vacuoles, blastomere asymmetry and multinucleation), to classify the embryos in accordance with ASEBIR criteria and to stipulate the clinical decision taken. In the EQC time-lapse pilot study, the groups were asked to determine, as well as the above characteristics, the embryo development times, the number, opposition and size of pronuclei, the direct division of 1 into 3 cells and/or of 3 into 5 cells and false divisions. The degree of agreement was determined by calculating the intra-class correlation coefficients and the coefficient of variation for the quantitative variables and the Gwet index for the qualitative variables. For both EmbryoScope™ and Primo Vision™, two periods of greater inter-laboratory variability were observed in the times of embryo development events. One peak of variability was recorded among the laboratories addressing the first embryo events (extrusion of the second polar body and the appearance of pronuclei); the second peak took place between the times corresponding to the 8-cell and morula stages. In most of the qualitative variables analysed regarding embryo development, there was almost-perfect inter-laboratory agreement among conventional morphological assessment (CMA), EmbryoScope™ and Primo Vision™, except for false divisions, vacuoles and asymmetry (users of all methods) and multinucleation (users of Primo Vision™), where the degree of agreement was lower. The inter-laboratory agreement on embryo classification according to the ASEBIR criteria was moderate-substantial (Gwet 0.41–0.80) for the laboratories using CMA and EmbryoScope™, and fair-moderate (Gwet 0.21–0.60) for those using Primo Vision™. The inter-laboratory agreement for clinical decision was moderate (Gwet 0.41–0.60) on day 5 for CMA users and almost perfect (Gwet 0.81–1) for time-lapse users. In conclusion, time-lapse technology does not improve inter-laboratory agreement on embryo classification or the analysis of each morphological variable. Moreover, depending on the time-lapse platform used, inter-laboratory agreement may be lower than that obtained by CMA. However, inter-laboratory agreement on clinical decisions is improved with the use of time lapse, regardless of the platform used.

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Section: Discussionmentioning

confidence: 99%

“…Some authors have suggested that the use of algorithms based on morphokinetic events should be complemented by the observation of classical morphological characteristics by TL [ 23 ] or by CMA [ 30 ]. However, our results show that this practice remains subject to inter-laboratory variability.…”

Section: Discussionmentioning

confidence: 99%

Inter-laboratory agreement on embryo classification and clinical decision: Conventional morphological assessment vs. time lapse

Martínez-Granados

Serrano²,

González-Útor³

et al. 2017

PLoS ONE

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“…The idea of using AI to aid decision-making for infertility treatment has been developed over many years, with an early proposal by Schild et al [62]. Despite increasing interest, AI is not routinely used by fertility clinics for making treatment decisions, but is starting to be used in infertility laboratories, with a number of tools for embryo monitoring and selection [63][64][65][66][67][68][69] currently available for objective judgment and choice of gametes and embryos using AI-picture analytics and decision-supporting algorithms [70]. The aim of these tools is to provide an objective recommendation for embryologists, enabling the standardization of treatment across different centers by removing operator variability/subjectivity.…”

Section: Future Perspectivesmentioning

confidence: 99%

Decision points for individualized hormonal stimulation with recombinant gonadotropins for treatment of women with infertility

Lunenfeld

Bilger

Longobardi

et al. 2019

Gynecological Endocrinology

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It is essential that fertility treatment is individualized based on a thorough diagnostic work-up, with treatment tailored to the patients' requirements. This individualization should be kept in mind during the main decision points that occur before and during treatment. Treatment customization must include consideration of both the woman and her partner involved in the process together, including their collective treatment goals. Once treatment goals have been agreed and diagnostic evaluations performed, personalization based on patient characteristics, together with an understanding of treatment goals and patient preferences, enables the selection of appropriate treatments, protocols, products and their dosing. Following treatment initiation, monitoring and adaptation of product and dose can then ensure optimal outcomes. Currently, it is not possible to base treatment decisions on every characteristic of the patient and personalization is based on biomarkers that have been identified as the most relevant. However, in the future, the use of artificial intelligence coupled with continuous monitoring should enable greater individualization and improve outcomes. This review considers the current state-of-the-art related to decision points during individualized treatment of female infertility, before looking at future developments that might further assist in making individualized treatment decisions, including the use of computer-assisted decision making. ARTICLE HISTORY

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“…Indeed, the success of vitrification of both oocytes and embryos, with close to 100% warming survival rates, has completely changed reproductive medicine for patients, with options for repeated ovarian stimulation/freeze all cycles in poor ovarian responders and in patients requiring multiple oocytes and embryos for relevant PGD or PGS ( Debrock et al , 2015 ; Blockeel et al , 2016 ; Rienzi et al , 2017 ). A wealth of industry based technology now allows the assessment of ovaries, gametes, embryos, endometrium and early pregnancy: advanced ultrasound imaging in 2D or 3D with associated computer algorithms ( Grozmann and Benacerraf, 2016 ), advanced molecular genetic assessment of oocytes and embryos ( Dahdouh et al , 2015 ; Fragouli and Wells, 2015 ), and continuous embryo monitoring and assessment of embryos at the moment of embryo transfer ( Adamson et al , 2016 ; Aparicio-Ruiz et al , 2016 ). Active research is aimed at better understanding and improving embryo implantation in the uterus after transfer ( Garcia-Velasco et al , 2016 ).…”

Section: The Essential Role Of Industry In Reproductive Medicine Todamentioning

confidence: 99%

Transparent collaboration between industry and academia can serve unmet patient need and contribute to reproductive public health

D’Hooghe

2017

Human Reproduction

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The pharmaceutical and device industry has greatly contributed to diagnostic and therapeutic approaches in reproductive medicine in a very highly regulated environment, ensuring that development and manufacturing follow the highest standards. In spite of these achievements, collaboration between industry and physicians/academia is often presented in a negative context. However, today more than ever, partnership between industry and academia is needed to shorten the timeline between innovation and application, and to achieve faster access to better diagnostics, drugs and devices for the benefit of patients and society, based on complementary knowledge, skills and expertise. Such partnerships can include joined preclinical/clinical and post-marketing research and development, joint intellectual property, and joint revenue. In Europe, the transparency of this collaboration between pharmaceutical industry and medical doctors has been made possible by the Compliance and Disclosure Policy published by the European Federation of Pharmaceutical Industries and Associations (EFPIA), which represents the major pharmaceutical companies operating in Europe, and includes as members some but not all companies active in infertility and women's health. Under the EFPIA Disclosure Code of conduct, companies need to disclose transfers of value including amounts, activity type and the names of the recipient Health Care Professionals and Organizations. EFPIA member companies have also implemented very strict internal quality control processes and procedures in the design, statistical analysis, reporting, publication and communication of clinical research, according to Good Clinical Practice and other regulations, and are regularly inspected by competent authorities such as the US Food and Drug Administration (FDA) or European Medicines Agency (EMA) for all trials used in marketing authorization applications. The risk of scientific bias exists not only in the pharmaceutical industry but also in the academic world. When academics believe in a hypothesis, they may build their case by emphasizing the arguments supporting their case, and either refute, refuse, oppose or ignore arguments that challenge their assumptions. A possible solution to reduce this bias is international consensus on study design, data collection, statistical analysis and reporting of outcomes, especially in the area of personalized reproductive medicine, e.g. to demonstrate superiority or non-inferiority of personalized ovarian stimulation using biomarkers. Equally important is that declarations of interest are reported transparently and completely in scientific abstracts and publications, and that ghost authorship is replaced by proactive and clear co-authorship for experts from industry where such co-authorship is required based on the prevailing ICMJE criteria. In that context, however, reviewers should stop believing that publications by industry authors only, or by mixed groups of co-authors from industry and academia, are more prone to bias than papers ...

show abstract

Automatic time-lapse instrument is superior to single-point morphology observation for selecting viable embryos: retrospective study in oocyte donation

Cited by 49 publications

References 27 publications

Inter-laboratory agreement on embryo classification and clinical decision: Conventional morphological assessment vs. time lapse

Inter-laboratory agreement on embryo classification and clinical decision: Conventional morphological assessment vs. time lapse

Decision points for individualized hormonal stimulation with recombinant gonadotropins for treatment of women with infertility

Transparent collaboration between industry and academia can serve unmet patient need and contribute to reproductive public health

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