P-241 ‘Augmented intelligence’ to possibly shorten euploid identification time: A human-machine interaction study for euploid identification using ERICA, an Artificial Intelligence software to assist embryo ranking
Authors: Chavez Badiola A , Flores-Saiffe A , Valencia R , Mendizabal-Ruiz G , Villavicencio J , Gonzalez D , Griffin D , Drakeley A , Cohen J 
Published: June 2022

Abstract

Study question
What is the mean number of transfers needed to achieve a euploid transfer selected by embryologists plus ERICA’s assistance?

Summary answer
Augmented intelligence (ERICA plus human collaboration) outperforms both the embryologists and artificial intelligence's individual performance alone.

What is known already
Euploid embryos are more likely to implant successfully. Artificial intelligence (AI) could improve embryo selection over current techniques, but scepticism exists. Augmented intelligence (AuI) combines both the mathematical reproducibility of machine learning and the knowledge and experience of humans. This approach employs AI tools as an assistant, where the user shall learn to interpret the AI. A recent study suggested that embryologists assisted by AI improved the embryo selection of euploid transfers. ERICA (IVF2.0 Limited, UK) was designed to rank blastocysts according to their probability of euploidy.

Study design, size, duration
We prospectively studied embryo selection for ERICA alone, embryologists only and when interacting (embryologists and ERICA) in 150 synthetically generated (reconstructed on real-data) embryo transfer cycles. Embryos were ranked in order, and performance was assessed by time to identify a euploid embryo within each cycle cohort correctly. Embryologists were allowed to rank a maximum of 10 cycles per day for three weeks starting in January 2022, using a mobile phone application designed for this purpose.

Participants/materials, setting, methods
Using real-life cycle distributions of euploid/aneuploid blastocysts and the number of embryos in a cycle (according to ERICA’s database), we created 150 synthetic cycles, 30 for each age bracket (&lt; 35, 35-37, 38-40, 41-42, and &gt;42). These were randomly populated with blastocyst images preserving their actual ploidy status correspondingly. Each synthetic cycle contained between 2 to 6 authentic embryo images with at least one euploid and one aneuploid.

Main results and the role of chance
The total database had a euploid rate of 37.4% (n = 513), and by age brackets from 1 to 5 were 45.7% (n = 116), 43.8% (n = 105), 35.9% (n = 92), 31.2% (n = 96), and 28.8% (n = 104) respectively.
The mean number of cycles analysed by each participant was 113.5 (CI: 100.8-126.2). The mean time-to-euploid transfer for embryologists alone was 2.07 (CI:2.00-2.13); for the ERICA alone was 1.86 (CI:1.82-1.91); and for embryologists assisted by ERICA was 1.62 (CI:1.55-1.68). All study groups compared to each other were statistically significant using a paired two-tailed student’s t-test (p &lt; 0.001).
The proportion of euploid transfer at the first try for embryologists alone was 0.40 (CI:0.37-0.43), for ERICA alone was 0.54 (CI:0.53-0.54), and for embryologists assisted by ERICA was 0.47 (CI:0.44-0.50). All study groups compared with each other were statistically significant with a paired two-tailed student’s t-test (p &lt; 0.01).

Limitations, reasons for caution
Although our findings suggest that Aul outperforms both AI and humans alone, this study needs to be replicated with a larger cohort of embryologists with different experience levels in different countries to confirm these results.

Wider implications of the findings
Combining machine-human interaction through a well-designed process could improve embryo selection and reduce inter-operator variability amongst staff with different experience levels. It could also set a frame for adequate agency and accountability, and enhance trust and adoption.

Trial registration number
NA