Together. All the way.

Mini-Series: How Terra Fertility is elevating care and supporting outcomes with EmbryoScope and PGT-A Plus  

Data from a recently published clinical study showed a significant increase in detection of euploid/diploid embryos in embryos with confirmed atypical PN using time-lapse technology with EmbryoScope and ploidy detection. 

A newly published study in F&S Reports explores a critical question in modern IVF: how does embryologist experience influence agreement with AI-driven embryo ranking, and what does this mean for clinical outcomes?

Mini-Series: How Terra Fertility is elevating care and supporting outcomes with EmbryoScope and PGT-A Plus  

In modern IVF practice, Time-lapse incubation with EmbryoScope+ and PGT-A Plus genetic testing offer complementary strategies to identify more usable embryos that may help improve transfer success.

Advancements improve identification of useable embryos, and transparency on the process

Co-author: Alexandra Frost and Vitrolife

Welcome to our blog post, where we explore the remarkable benefits of integrating time-lapse technology and specialised software in clinical settings. By combining these innovative tools, clinics worldwide are optimising workflows, gaining valuable insights, and revolutionising patient care. Carol Loscher, Laboratory Manager at Thérapie Fertility in Dublin, sharesthe valuable insight of their practice in the clinic.

In 2020, Nij Geertgen (Netherlands) became the first center in Benelux to offer 100% time-lapse for all patients.  

What is artificial intelligence?

Artificial intelligence is widely used and affects our daily lives even if we do not notice. From smart phone tools and personalised shopping experiences to assisted airline operations and medical diagnosis. When the appropriate method is chosen and trained on data that sufficiently represents the intended use, artificial intelligence makes our life easier and more comfortable.

Blastocyst grading is known to be highly subjective and without the use of time-lapse can result in an incorrect evaluation of collapsed or partially collapsed blastocysts. Artificial intelligence has the potential to make processes in IVF more precise, consistent and efficient in the future. This paper¹ describes how deep learning (a subset of artificial intelligence) was used to design an algorithm to enable fully automatic grading of ICM and TE quality from time-lapse images. A similar approach has been used to estimate morphokinetic timings and PN count.

No one can argue against the value of high-quality, correctly designed, appropriately powered, multicentre prospective RCTs (or even better systematic reviews of a number of such studies) as the ideal mechanism to generate evidenced-based medicine. But I’m afraid the assisted conception world is far from ideal in this respect and high-quality studies are notoriously difficult to carry out. Unfortunately, some might consider this the situation with evidence in support of the EmbryoScope and morphokinetic embryo selection algorithms, although I’m not sure I’d entirely agree. 

When working in an IVF lab there are different ways to assess embryos. You can score embryos using morphology and traditional grading, you can use preimplantation genetic testing (PGT) to get the genetic profiling of embryos prior to implantation, and finally you can analyse morphokinetic parameters of embryo development using time-lapse information. How do we bring together morphology, PGT and time-lapse to improve our embryo selection?