O-073 Artificial intelligence (AI) based triage for preimplantation genetic testing (PGT); an AI model that detects novel features in the embryo associated with ploidy. (30th June 2022) - Meseguer Escriva, M
Abstract: Study question: Can an AI based triage system noninvasively detect aneuploidy in preimplantation embryos in a precise and valid manner? Summary answer: Using a feature extraction approach to identify features in time-lapse images, an AI model was validated and found to noninvasively detect ploidy with unprecedented accuracy. What is known already: Invasive PGT with trophectoderm biopsy is the gold standard for evaluating the genetic integrity of an embryo prior to transfer. Even so, its utility and diagnostic accuracy is debated due to concern of structural damage, sampling bias and viability after vitrification-warming. Though several noninvasive methods for evaluating ploidy have been developed, their main limitations lay in their accuracy. This study reports on the ongoing validation of an AI model that relies on feature extraction and thresholding techniques to distinguish between aneuploid and euploid embryos; the model is intended to be used in clinical settings for PGT triage and preferential transfer. Study design, size, duration: In this single-center study, we used a retrospective dataset consisting of time-lapse images from 2, 502 preimplantation embryos with known ploidy status to train and validate the AI model. Participants/materials, setting, methods: The model utilized videos captured from time-lapse incubator (Embryoscope) up to 144 hours post-fertilization with chromosome analysis performed using next-generation sequence technology as ground truth labels. The data set was divided using an 70/15/15 training-validation-test split of the data. The AI model included convolutional neural network extracted features alongside spatial features based on several biological and clinical characteristics known to associate with ploidy, embryo behavior, and function. Performances were measured by validation and test-set accuracy. Main results and the role of chance: Five feature modules were included in the AI model for ploidy evaluation. All modules were analyzed separately and combined: (I) automated detection of abnormal morphokinetic patterns (t2-t8, tM, tSB, tB, tHB) differentiated between the two classes (aneuploid and euploid) to predict aneuploidy with an accuracy of 52%, p < 0.05; (II) previously validated embryo grading classification algorithm demonstrated an association between A and C-grade embryos with euploidy and aneuploidy, respectfully, with an accuracy of 68%, p < 0.05; (III) differential cell division activity and compaction between the two classes predicted aneuploidy with an accuracy of 73%, p < 0.05; (IV): AI-based classification of mitochondrial DNA content, measured as 0.5 micron irregularities in time-lapse images, predicted aneuploidy with an accuracy of 77%, p < 0.05; blastocoelic contractions of more than 8 microns in diameter predicted aneuploidy with 56% accuracy, p < 0.05. Using our AI model, we were able to integrate all 5 features, thereby achieving an unprecedented 90% accuracy. Two features – detection of abnormal morphokinetic patterns and blastocoelic contractions – occur in a minority of embryos (in 3% and 20% of all embryos in the database, respectively). When they do occur, they independently predict aneuploidy with an accuracy of 90% and 82%, demonstrating the robustness of our multi-feature model. Limitations, reasons for caution: Our AI model needs to be tested on a large, multi-centric dataset to ensure standardization and ability to be replicated in different settings. Even so, given our high degree of demonstrated accuracy, we conclude that our single-center dataset was sufficient for developing the initial validation of the model reported here. Wider implications of the findings: The 'explainability' and implementation of our AI model enables more objective embryo quality assessment and improves the clinics' ability to prioritize embryos for PGT and preferential transfer using a validated and trusted framework that reduce dramatically the chances of transferring an aneuploid embryo to our patients. Trial registration number: not applicable