P–141 Artificial intelligence system for the automation of the blastocyst morphology evaluation in GERI Time-lapse Incubator
Authors: Pay. Bosch E , Bori L , Beltran A , Naranjo V , Meseguer M 
Published: August 2021

Abstract

Study question
Can an Artificial Intelligence (AI) system (hand-crafted vs. deep learning techniques) based on single embryo image analysis from a GERI time-lapse incubator (TL) evaluate the blastocyst morphology?

Summary answer
Our hand-crafted method trained with blastocyst images from Geri-TL evaluated and classified parameters regarding to embryo quality with a global precision of 63.7% in blind-test.

What is known already
Recent studies have shown that AI can improve automatic grading and embryo selection. The approaches that have been carried out are very different, but all they conclude that there is a great potential (Rad2019, Manoj2020, Thirumalaraju2020). As we know, conventional embryo evaluation is performed manually based on the morphology of the blastocyst, therefore, it should be possible to replicate this process. In this study, we implemented different methods to analyse the behaviour and performance of an AI doing embryology tasks.

Study design, size, duration
Our study consisted of a retrospective analysis for the automatization of embryo evaluation with different approaches. We developed our models based on 715 images extracted from GERI TL Videos (Genea, Australia) from a single IVF center. Database was divided into 3 classes depending on the quality of the embryo according to ASEBIR morphology criteria (high; medium and low-quality). All the images were divided into 70% for training, 15% for validating and 15% for testing.

Participants/materials, setting, methods
We developed an automated AI algorithm to extract and classify features from images at 111,5 hpi of embryos cultured in GERI TL. Hand-crafted features from texture information are extracted to feed the classification algorithm. A statistical analysis is carried out to select the more discriminative variables. Parallelly, a deep neural network was built to compare performance of automatic and hand-crafted features. Additionally, we trained a model to detect embryo in the well.

Main results and the role of chance
High-quality, medium-quality and low-quality sensitivity were 73%, 56% and 72% for hand-crafted method and 76%, 53% and 22% for deep learning approach, respectively. High-quality, medium-quality and low-quality precision were 66%, 56% and 76% for hand-crafted method and 40%, 60% and 55% for deep learning approach, respectively. The global accuracy associated with each method was 64% and 50%. Also, we noticed that results were higher when we applied our embryo masks that avoid irrelevant information. In this initial attempt, our results showed that it is possible to replicate the embryo evaluation process.

Limitations, reasons for caution
The low results obtained in our deep learning model due to the absence of an extent dataset did not allow to obtain a model applicable to the clinic. However, the preliminary study let us to conclude the high potential of the approach.
Wider implications of the findings: Our results showed a potential automatization of the embryo evaluation process in Geri TL where the available software for embryo selection does not provide such option. Our findings leaded to an increase in objectification, a reduction of the workload of the embryologist and the research of new unknown morphological variables.

Trial registration number
Not applicable