O-007 Simplifying the complexity of time-lapse decisions with AI: CHLOE (Fairtility) can automatically annotate morphokinetics and predict blastulation (at 30hpi), pregnancy and ongoing clinical pregnancy
Authors: Yelke H K , Ozkara G , Yuksel B , Kumtepe Colakoglu Y , Aygun M , Brualla A , Erlich I , Hickman C , Selimoglu S , Okten B , Kahraman S 
Published: June 2022

Study question

What is CHLOE’s (Fairtility) efficacy of prediction of blastulation (at 30hpi), pregnancy and ongoing clinical pregnancy following single embryo transfer (SET)?

Summary answer

CHLOE(Fairtility) algorithms are effective predictors of blastulation, ploidy, pregnancy, implantation and ongoing clinical pregnancy

What is known already

Time-lapse incubators have increased the amount of information available to the embryologist to help determine the fate of embryos. This has led to differences in clinical practice between clinics in how this information is prioritised. Moreover, inter-operator inconsistencies and the time-consuming nature of manually annotating time-lapse videos are challenges currently experienced by time-lapse users that can be relieved with Artificial Intelligence(AI) tools, such as CHLOE(Fairtility). CHLOE levergaes AI-based predictors to predict blastulation and implantation, whilst providing transparency to which biological characteristics have led to that determination. There is a need to validate AI tools before their incorporation into clinical practice.

Study design, size, duration

This was a single centre study that took place between 2017-2020, at Istanbul Memorial Sisli Hospital in Turkey, ART and Center. This was a retrospective cohort analysis that reviewed 6748 time-lapse videos containing 5392 cleaved embryos, 3763 blastocysts, 877 single embryo transfers(SET) with known ongoing pregnancy outcome (KOPO), 306 euploid SETs and 25 mosaic embryo SETs with KOPO. CHLOE blastocyst and implantation score efficacy of prediction of clinical outcomes was quantified using the metric AUC.

Participants/materials, setting, methods

Time-lapse videos were assessed using CHLOE(Fairtility), an AI based tool, to quantify quantitative and qualitative morphokinetics (including automated annotations of tPNa,tPNf,t2,t3,t4,t5,t6,t7,t8,t9,tM,tSB,tB,tEB), CHLOE implantation score and CHLOE blastocyst score (calculated at 30hpi) relative to laboratory (ploidy results, blastulation) and clinical outcomes (biochemical, clinical and ongoing pregnancy) following overall SET. Binary logistic regression was used to calculate area under the curve (AUC) as a measure of prediction efficacy.

Main results and the role of chance

Blastulation score assessment of cleaved embryos was predictive of blastulation (AUC=0.96, baseline=70% n = 5392, p &lt; 0.001). Following PGT-A, implantation score was predictive of euploids (AUC=0.61, baseline=34%, n = 1456, p &lt; 0.001), but not of embryos classified as mosaics (AUC=0.5, baseline=19%, n = 1456, p &gt; 0.05). Following SET, implantation score was predictive of biochemical (AUC=0.71, baseline=49%, n = 866, p &lt; 0.001), clinical and ongoing pregnancy rate (AUC=0.69, baseline=37%, n = 866, p &lt; 0.001). Following SET of non-PGT-A embryos, implantation score decreased with increasing patient age (p &lt; 0.001). The type of aneuploidy (such as monosomy, trisomy, segmental) did not affect implantation score or blastulation score (p &gt; 0.05). Implantation score prediction of outcome was higher for non-PGT-A transfers than overall transfers for biochemical (Non-PGTA: AUC=0.73, baseline=33%, n = 535, p &lt; 0.001; OVERALL: AUC=0.71, baseline=49%, n = 866, p &lt; 0.001), clinical and ongoing pregnancy (Non-PGTA: AUC=0.76, baseline=24%, n = 535, p &lt; 0.001; OVERALL: AUC=0.69, baseline=37%, n = 866, p &lt; 0.001), despite lower baselines.

Limitations, reasons for caution

This is a single centre study, using retrospective data where embryos were selected for transfer by human embryologists. Despite the data has heterogeneity in terms of clinical features, the study is part of a larger framework for responsible incorporation of AI into clinical practice through robust validation.

Wider implications of the findings

AI-based tools have the potential of increasing consistency, efficiency and efficacy of embryo selection. The additional information on quantitative and qualitative morphokinetics that AI tools such as CHLOE provide, bring transparency to the prediction, allowing for improvement in personalisation of care down to each individual embryo.

Trial registration number

None