O-121 Exploring non-invasive methods to predict Ploidy Status: Combination of blastocyst morphology image analysis and proteomic profiles by using Artificial Neural Networks
Authors: Garg A , Bari L , Valera M A , Fernandez E I , Rocha J C , Quiñonero A , Domínguez F , Meseguer M 
Published: August 2021

Study question

Is the blastocyst morphology image analysis combined with the protein content of spent embryo culture medium a suitable way to predict embryo ploidy?

Summary answer

Morphological variables from blastocyst image analysis combined with IL-6 or MMP-1 concentration in spent culture medium showed more than 80% of accuracy for euploidy prediction.

What is known already

An artificial intelligence model based on the proteomic profile of euploid embryos and morphological data from blastocyst time-lapse images has been recently published (Bori et al., 2020). The most promising artificial neural network (ANN) algorithm considered 20 morphological variables extracted from image analysis and two proteins detected in embryo culture medium (MMP-1 and IL-6). The overall success rate on blind test data was 72.7% for live birth prediction. The main aim of the present study was to check if the same morphological variables combined with MMP-1 or IL-6 with a cost-effective technique could discriminate between euploid and aneuploid embryos.

Study design, size, duration

This prospective study included 120 embryos from the preimplantation genetic testing for aneuploidies (PGT-A) program. A single blastocyst image was obtained for each embryo and their spent culture medium was collected on the day 5/6 of embryo development (day of trophectoderm biopsy). Morphological variables were extracted for all the blastocyst. On the other hand, we quantified IL-6 levels of 67 embryos and MMP-1 levels of 53 embryos. Resulting parameters were used to predict PGT-A results.

Participants/materials, setting, methods

Blastocyst images were imported into Matlab software and segmented into regions of interest. We obtained 20 mathematical variables related to measurements of areas, number of pixels and texture analysis. Chromosome analysis was performed using next-generation sequence technology. In parallel, 20 µL of spent culture medium from each blastocyst was analyzed with ELISA kits (IL-6 or MMP-1). Protein concentrations and morphological variables were used as input data for an ANN associated with genetic algorithms.

Main results and the role of chance

The euploid rate for the set of embryos included in the IL-6 group was 51.4%. The ANN was trained with 49 embryos and blind tested with 18 embryos. Following results correspond to euploidy prediction on the blind test. The sensitivity, specificity, accuracy and area under the ROC curve (AUC) were: 0.56, 0.78, 0.67 and 0.72 considering only IL-6 values; 0.88, 0.78, 0.83 and 0.61 considering IL-6 values and blastocyst morphological data extracted from the image analysis. The euploid rate for the set of embryos included in the MMP-1 group was 51.9%. The ANN was trained with 39 embryos and blind tested with 14 embryos. Following results correspond to euploidy prediction on the blind test. The sensitivity, specificity, accuracy and AUC were: 0.71, 0.57, 0.64 and 0.67 considering only MMP-1 values; 0.86, 0.86, 0.86 and 0.61 considering MMP-1 values and morphological data extracted from the image analysis.

Limitations, reasons for caution

The detection limit in protein quantification is the main limitation of our study. The small number of embryos and the specific culture medium used should be considered for the model application.

Wider implications of the findings

Our preliminary results showed that blastocyst morphology and embryo secretomics could be useful for euploidy prediction by using artificial intelligence techniques. These findings may contribute to the emerging era of non-invasive preimplantation genetic testing (ni-PGT-A).

Trial registration number

not applicable