Can the combination of time-lapse parameters and clinical features predict embryonic ploidy status or implantation?

Zou, Yaoyu; Pan, Yingxia; Ge, Naidong; Xu, Yan; Gu, Ruihuan; Li, Zhichao; Fu, Jing; Gao, Junhui; Sun, Xiao; Sun, Yijuan

doi:10.1016/j.rbmo.2022.06.007

Cited by 11 publications

(10 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zou et al's recent study emphasizes that clinical features can largely improve embryo prediction performance, and their combination with TLM parameters is robust to predict high-grade euploid blastocysts. The models for ploidy prediction, however, were not highly predictive, suggesting they cannot replace preimplantation genetic testing currently 1 . New methodology, involving deep machine learning enriched with multi-centric clinical data, where every image from the time-lapse documentation is analyzed by an algorithm, looking for patterns that link to outcome, may in the future provide a more accurate and non-biased embryo selection process 2 .…”

Section: Introductionmentioning

confidence: 93%

“…Input variables included estradiol concentrations in picograms per milliliter; ultrasound measurements of follicle diameters in two dimensions in millimeters; cycle day during stimulation and dose of recombinant follicle-stimulating hormone during ovarian stimulation for IVF. The main outcome measures included accuracy of the algorithm to predict four critical clinical decisions during ovarian stimulation for IVF: [1] stop stimulation or continue stimulation. If the decision was to stop, then the next automated decision was to [2] trigger or cancel.…”

Section: Artificial Intelligence Aided Algorithm For Personalized Ova...mentioning

confidence: 99%

“…Indeed, it is generally acknowledged that even after embryo selection based on morphology, timelapse microscopic photography, or embryo biopsy with preimplantation genetic testing (PGT-A), implantation rates in the human are difficult to predict. Recently, several artificial intelligence (AI)-based methods have emerged as objective, standardized, and efficient tools for evaluating human embryos 1,2 . Artificial Intelligence (AI) and Machine Learning (ML) are clearly emerging technologies in Medically Assisted Reproduction (MAR) and would benefit from early application of reporting standards.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Embryo Transfer is the last Frontier for Deep Machine Learning & Artificial Intelligence in Medically Assisted Reproduction (MAR)

Allahbadia

2023

TJOR

View full text Add to dashboard Cite

Embryo Transfer is the last Frontier for Deep Machine Learning & Artificial Intelligence in Medically Assisted Reproduction (MAR). In the last five years, nearly every aspect of an IVF cycle has been investigated by artificial intelligence(AI) & deep machine learning (ML), including sperm morphology, sperm identification, identification of empty or oocyte containing follicles, prediction of embryo cell stages, prediction of blastulation from oocytes, scoring blastocyst quality, prediction of euploid blastocysts and live birth from blastocysts, improving the embryo selection process, and for developing algorithms for optimal IVF stimulation protocols. Moreover, AI-based methods can be implemented for other clinical aspects of IVF, such as assessing patient reproductive potential and individualizing gonadotropin stimulation protocols. As AI has the capability to analyze "big" data, the ultimate goal will be to apply AI tools to the analysis of all embryological, clinical, and genetic data in an effort to provide patient-tailored treatments. Embryo Transfer is the only step of IVF that is outside the realm of AI & ML today. Embryo Transfer success is presently human skill dependent and deep machine learning may one day intrude into this sacred space with the advent of specialized humanoid robots. Embryo transfer is arguably the rate limiting step in the sequential events that complete an IVF cycle. Many variables play a role in the success of embryo transfer, including catheter type, atraumatic technique, and the use of sonography guidance. In this clinical review we will cover the contemporary research goals of AI & ML as well as the variables influencing Embryo Transfer success.

show abstract

Section: Introductionmentioning

confidence: 93%

Section: Artificial Intelligence Aided Algorithm For Personalized Ova...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Embryo Transfer is the last Frontier for Deep Machine Learning & Artificial Intelligence in Medically Assisted Reproduction (MAR)

Allahbadia

2023

TJOR

View full text Add to dashboard Cite

show abstract

“…Therefore, considering more visual parameters will enhance the effectiveness of the embryo evaluation system. Combining embryo kinetic parameters based on time-lapse imaging techniques will greatly improve the accuracy of the embryo evaluation system, as even a small difference in the timing of dynamic embryonic development can lead to signi cant changes [15]. However, this will increase the di culty of the evaluation system and make it more complex.…”

Section: Introductionmentioning

confidence: 99%

A new day 4 embryo evaluation criteria to predict the formation of blastocyst

Dai,

Yasong,

Tao

et al. 2024

Preprint

View full text Add to dashboard Cite

Background Currently, in vitro fertilization and embryo transfer (IVF-ET) typically involve transferring embryos on the third (D3) or fifth day (D5). However, a fresh cycle fourth day (D4) morula transfer offers a flexible and effective alternative. The compaction on the fourth day serves as a simple and reliable indicator to predict embryo implantation potential. The use of D4 transfer is gradually gaining popularity. Nevertheless, there is still a need for a comprehensive evaluation system for fourth day embryos.Thus, this study proposes to explore a day 4 embryo evaluation system based on the potential for the embryo to develop into a transferableblastocyst. Methods We observed the outcome of blastocyst culture for 1235 embryos from 199 patients. We considered the developmental stage, fragmentation, whether it was in a stage-specific cleavage pattern, and normal fertilization on the fourth day. The fourth day embryo evaluation system was proposed by comparing them with the effect on blastocyst formation rate. The rates of blastocyst formation with different embryo grades were compared. The receiver operative characteristics(ROC) curve was used to compare the predictive abilities of four criteria for day 4 evaluation to develop into transferableblastocysts and high-quality blastocysts. Results The developmental stage, fragmentation, normal fertilization and stage-specific cleavage pattern had the greatest impact on the formation of transferable blastocysts. There were significant differences in the formation rates of transferableblastocysts and high-quality blastocysts among different embryo grades. The areas under ROC curves of the day 4 embryo evaluation criteria constructed in this study was the highest in predicting formation of transferable or high-quality blastocyst, relative to the other three evaluation criteria. This study observed the presence of pseudo-compacted embryos similar to cleavage-stage embryos on the rate of blastocyst formation. Conclusion The Day 4 embryo evaluation criteria constructed in this study can effectively predict the ability to develop into a blastocyst.

show abstract

“…Therefore, considering more visual parameters will enhance the effectiveness of the embryo evaluation system. Combining embryo kinetic parameters based on time-lapse imaging techniques will greatly improve the accuracy of the embryo evaluation system, as even a small difference in the timing of dynamic embryonic development can lead to significant changes [15] . However, this will increase the difficulty of the evaluation system and make it more complex.…”

Section: Introductionmentioning

confidence: 99%

A new day 4 embryo evaluation system to predict the formation of transferable blastocyst

Dai,

Geng,

Tao

et al. 2024

Preprint

View full text Add to dashboard Cite

Purpose To explore a day 4 embryo evaluation system based on the potential for the embryo to develop into a transferable blastocyst. Methods We observed the outcome of blastocyst culture for 1235 embryos from 199 patients. We considered the developmental stage, fragmentation, whether it was in a stage-specific cleavage pattern, and normal fertilization to formulate scoring criteria, based on whether they developed into transferable blastocysts. The receiver operative characteristics(ROC) curve was used to compare the predictive abilities of four criteria for day 4 evaluation to develop into transferable blastocysts and high-quality blastocysts. Results The developmental stage, fragmentation, normal fertilization and stage-specific cleavage pattern had the greatest impact on the formation of transferable blastocysts. There were significant differences in the formation rates of transferable blastocysts and high-quality blastocysts among different embryo grades. The areas under ROC curves of the day 4 embryo evaluation criteria constructed in this study was the highest in predicting transferable blastocyst formation, relative to the other three evaluation criteria. Conclusion This study observed the presence of pseudo-compacted embryos similar to cleavage-stage embryos. The Day 4 embryo evaluation criteria constructed in this study can effectively predict the formation of transferable blastocysts.

show abstract

Can the combination of time-lapse parameters and clinical features predict embryonic ploidy status or implantation?

Cited by 11 publications

References 39 publications

Embryo Transfer is the last Frontier for Deep Machine Learning & Artificial Intelligence in Medically Assisted Reproduction (MAR)

Embryo Transfer is the last Frontier for Deep Machine Learning & Artificial Intelligence in Medically Assisted Reproduction (MAR)

A new day 4 embryo evaluation criteria to predict the formation of blastocyst

A new day 4 embryo evaluation system to predict the formation of transferable blastocyst

Contact Info

Product

Resources

About