P-277 Multimodal deep learning for embryo ploidy prediction in assisted reproduction
P Cao, E Klerkx, J Derhaag, E Coonen, G Acharya, A Salumets, M Zamani EstekiAbstract
Study question
Can a multimodal deep learning approach integrating time-lapse imaging, clinical parameters, and genomic data predict embryo ploidy status in assisted reproductive technology (ART)?
Summary answer
The multimodal deep learning model can effectively predict ploidy status of embryos using merely embryo imaging.
What is known already
Current embryo selection relies on subjective morphological assessment or invasive preimplantation genetic testing for aneuploidy (PGT-A). Although PGT-A can diagnose embryo ploidy status and is claimed to enhance pregnancy rates, it presents several limitations including invasive biopsy, high cost, and potentially inaccuracies in detecting mosaicism. Recent artificial intelligence (AI) models show promise but primarily focus on viability prediction rather than genome-wide chromosomal analysis. Despite advances in time-lapse imaging and AI, no validated model currently integrates multimodal data for non-invasive ploidy prediction. Furthermore, existing methods lack the capability to distinguish between meiotic and mitotic chromosomal abnormalities, crucial for understanding developmental potential.
Study design, size, duration
We retrospectively analyzed 1,400 embryos from PGT cycles (2022-2024) using a stratified random sampling approach. We used ResNet152 as backbone to develop the model, using a 70/20/10 split for training, validation, and testing. Multiple logistic regression and bootstrapping (1000 iterations) were performed for statistical validation. Model performance was assessed using ROCAUC analysis.
Participants/materials, setting, methods
We included embryos from patients undergoing PGT at our center, cultured in EmbryoScope® time-lapse incubators. Imaging frames were extracted at 2-hour intervals. Morphokinetic parameters (from fertilization to blastocyst formation) and imaging texture features, e.g., density were analyzed. Chromosomal status was determined via whole-genome sequencing of trophectoderm biopsies using haplarithmisis. We then applied class activation mapping (CAM) heatmaps to visualize pixel regions predictive of ploidy outcomes for model interpretation.
Main results and the role of chance
The multimodal model achieved a ploidy prediction ROCAUC of 0.72, significantly outperforming morphology-only model (ROCAUC: 0.67, p < 0.001). Texture analysis revealed significant correlations between density patterns and ploidy status (Spearman’s ρ = 0.68, p < 0.001). Texture feature scores were significantly different across embryo development process, such as key indicators at fertilization, day3, and day5. CAM heatmap analysis identified inner cell mass region as the strongest morphological predictor.
Limitations, reasons for caution
Our study has limitations due to its retrospective nature and the single-center design, which may restrict generalizability. Selection bias may occur as we only included embryos progressing to biopsy. External validation across different laboratories and diverse datasets is needed to confirm these findings.
Wider implications of the findings
Our method demonstrated the potential to reduce reliance on invasive PGT-A, mitigating procedural risks and costs. By enabling non-invasive euploid embryo selection, we aim to enhance the accessibility to precision reproductive care and refine transfer strategies for chromosomal abnormal embryos.
Trial registration number
No