Assessing human blastocyst quality using artificial intelligence (AI) convolutional neural network (CNN)

Zaninović, Nikica; Khosravi, Pegah; Hajirasouliha, Iman; Malmsten, Jonas; Kazemi, Ehsan; Toschi, M.; Elemento, Olivier; Rosenwaks, Zev

doi:10.1016/j.fertnstert.2018.07.267

Cited by 7 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Strategic collaboration with other disciplines and approaches will be crucial to ensure progress in this field. Being a dynamic process and extremely difficult to observe even with the aid of TLM, the morula stage could be amenable to investigation by exploiting machine learning approaches, which has already found application to predict embryo implantation ability at the blastocyst stage (Zaninovic et al, 2018). Further progress could derive from the analysis of extruded/excluded cells to reveal their cellular and molecular constitution and gain more in-depth insights on possible mechanisms of selfcorrection.…”

Section: A Plea For More Research On the Morula Stage And Compaction Processmentioning

confidence: 99%

The enigmatic morula: mechanisms of development, cell fate determination, self-correction and implications for ART

Coticchio¹,

Lagalla²,

Sturmey

et al. 2019

Human Reproduction Update

View full text Add to dashboard Cite

BACKGROUND Assisted reproduction technology offers the opportunity to observe the very early stages of human development. However, due to practical constraints, for decades morphological examination of embryo development has been undertaken at a few isolated time points at the stages of fertilisation (Day 1), cleavage (Day 2–3) and blastocyst (Day 5–6). Rather surprisingly, the morula stage (Day 3–4) has been so far neglected, despite its involvement in crucial cellular processes and developmental decisions. OBJECTIVE AND RATIONALE The objective of this review is to collate novel and unsuspected insights into developmental processes occurring during formation of the morula, highlighting the key importance of this stage for a better understanding of preimplantation development and an improvement of ART. SEARCH METHODS PubMed was used to search the MEDLINE database for peer-reviewed English-language original articles and reviews concerning the morula stage in mammals. Searches were performed by adopting ‘embryo’, ‘morula’, ‘compaction’, ‘cell fate’ and ‘IVF/assisted reproduction’ as main terms, in association with other keywords expressing concepts relevant to the subject (e.g. cell polarity). The most relevant publications, i.e. those concerning major phenomena occurring during formation of the morula in established experimental models and the human species, were assessed and discussed critically. OUTCOMES Novel live cell imaging technologies and cell biology studies have extended our understanding of morula formation as a key stage for the development of the blastocyst and determination of the inner cell mass (ICM) and the trophectoderm (TE). Cellular processes, such as dynamic formation of filopodia and cytoskeleton-mediated zippering cell-to-cell interactions, intervene to allow cell compaction (a geometrical requisite essential for development) and formation of the blastocoel, respectively. At the same time, differential orientation of cleavage planes, cell polarity and cortical tensile forces interact and cooperate to position blastomeres either internally or externally, thereby influencing their cellular fate. Recent time lapse microscopy (TLM) observations also suggest that in the human the process of compaction may represent an important checkpoint for embryo viability, through which chromosomally abnormal blastomeres are sensed and eliminated by the embryo. WIDER IMPLICATIONS In clinical embryology, the morula stage has been always perceived as a ‘black box’ in the continuum of preimplantation development. This has dictated its virtual exclusion from mainstream ART procedures. Recent findings described in this review indicate that the morula, and the associated process of compaction, as a crucial stage not only for the formation of the blastocyst, but also for the health of the conceptus. This understanding may open new avenues for innovative approaches to embryo manipulation, assessment and treatment.

show abstract

Section: A Plea For More Research On the Morula Stage And Compaction Processmentioning

confidence: 99%

The enigmatic morula: mechanisms of development, cell fate determination, self-correction and implications for ART

Coticchio¹,

Lagalla²,

Sturmey

et al. 2019

Human Reproduction Update

View full text Add to dashboard Cite

show abstract

“…As a result of this, continuity of the development of observations could be applied in the form of an ANN architecture to evaluate quality of embryos using morphokinetic and morphological events that achieved an 83% accuracy as shown by Zaninovic et al 72 also showed the application of an Inception-V1 algorithm to improve the parameters to obtain a 97.6% accuracy to discriminate between groups of poor and good blastocysts by way of using 50,392 images and selecting only 10,148 embryos using the time-lapse system. Despite this evidence, time-lapse hardware use is prohibited in most laboratories due to the lack of statistically significant clinical trial data to show the possible superiority of this software in comparison to conventional methods.…”

Section: Ob/gyn-mh Sequelaementioning

confidence: 99%

Artificial intelligence: A rapid case for advancement in the personalization of Gynaecology/Obstetric and Mental Health care

Delanerolle

Yang

Shetty

et al. 2021

Womens Health (Lond Engl)

View full text Add to dashboard Cite

To evaluate and holistically treat the mental health sequelae and potential psychiatric comorbidities associated with obstetric and gynaecological conditions, it is important to optimize patient care, ensure efficient use of limited resources and improve health-economic models. Artificial intelligence applications could assist in achieving the above. The World Health Organization and global healthcare systems have already recognized the use of artificial intelligence technologies to address ‘system gaps’ and automate some of the more cumbersome tasks to optimize clinical services and reduce health inequalities. Currently, both mental health and obstetric and gynaecological services independently use artificial intelligence applications. Thus, suitable solutions are shared between mental health and obstetric and gynaecological clinical practices, independent of one another. Although, to address complexities with some patients who may have often interchanging sequelae with mental health and obstetric and gynaecological illnesses, ‘holistically’ developed artificial intelligence applications could be useful. Therefore, we present a rapid review to understand the currently available artificial intelligence applications and research into multi-morbid conditions, including clinical trial-based validations. Most artificial intelligence applications are intrinsically data-driven tools, and their validation in healthcare can be challenging as they require large-scale clinical trials. Furthermore, most artificial intelligence applications use rate-limiting mock data sets, which restrict their applicability to a clinical population. Some researchers may fail to recognize the randomness in the data generating processes in clinical care from a statistical perspective with a potentially minimal representation of a population, limiting their applicability within a real-world setting. However, novel, innovative trial designs could pave the way to generate better data sets that are generalizable to the entire global population. A collaboration between artificial intelligence and statistical models could be developed and deployed with algorithmic and domain interpretability to achieve this. In addition, acquiring big data sets is vital to ensure these artificial intelligence applications provide the highest accuracy within a real-world setting, especially when used as part of a clinical diagnosis or treatment.

show abstract

“…More specifically, one of those studies achieved 83% overall accuracy in predicting live birth by looking at 386 time-lapse images of single blastocyst transfers (23). Another study reviewed 50,392 images from 10,148 embryos and managed to obtain 97.53% accuracy in discriminating between a poor and good blastocyst (24). Others used pre-treatment characteristics of known cycles to predict first cycle success, which had an accuracy of 81% (25).…”

Section: Ai In Ivfmentioning

confidence: 99%

Artificial Intelligence in Obstetrics and Gynaecology: Is This the Way Forward?

Emin

Papalois

et al. 2019

In Vivo

View full text Add to dashboard Cite

Assessing human blastocyst quality using artificial intelligence (AI) convolutional neural network (CNN)

Cited by 7 publications

References 0 publications

The enigmatic morula: mechanisms of development, cell fate determination, self-correction and implications for ART

The enigmatic morula: mechanisms of development, cell fate determination, self-correction and implications for ART

Artificial intelligence: A rapid case for advancement in the personalization of Gynaecology/Obstetric and Mental Health care

Artificial Intelligence in Obstetrics and Gynaecology: Is This the Way Forward?

Contact Info

Product

Resources

About