Deep learning system for classification of ploidy status using time-lapse videos.

Autores de IIS La Fe

• Lorena Bori Arnal

  Autor

• 

  Marcos Meseguer Escrivá

  Autor

Participantes ajenos a IIS La Fe

• Paya, Elena
• Pulgarin, Cristian
• Colomer, Adrian
• Naranjo, Valery

Grupos

• Biomarcadores, Medicina Genómica, Estadística y Análisis masivo de datos en Reproducción humana asistida

  

  Investigador Principal

  Marcos Meseguer Escrivá

Abstract

OBJECTIVE: To develop a spatiotemporal model for de prediction of euploid and aneuploid embryos using time-lapse videos from 10 to 115 hours post-insemination (hpi). DESIGN: Retrospective study. MAIN OUTCOME MEASURES: The research used an end-to-end approach to develop an automated AI system capable of extracting features from images and classifying them, considering spatio-temporal dependencies. (1) A convolutional neural network (CNN) extracted the most relevant features from each video frame. (2) A bidirectional long short-term memory (LSTM) layer received this information and analyzed the temporal dependencies, obtaining a low-dimensional feature vector that characterized each video. (3) A multilayer perceptron classified them into two groups, euploid and non-euploid. RESULTS: The model performance in accuracy fell between 0.6170 and 0.7308. A multi-input model with a gate recurrent unit (GRU) module performed better than others; the precision (or positive predictive value) is 0.8205 for predicting euploidy. Sensitivity, specificity, F1-Score and accuracy are 0.6957, 0.7813, 0.7042, and 0.7308, respectively. CONCLUSIONS: This paper proposes an artificial intelligence solution for prioritizing euploid embryo transfer. We can highlight the identification of a non-invasive method for chromosomal status diagnosis using a deep learning approach that analyzes raw data provided by time-lapse incubators. This method demonstrated potential automation of the evaluation process, allowing spatial and temporal information to encode.

Copyright © 2023. Published by Elsevier Inc.

Keywords

• Euploid; PGT-A; artificial intelligence; computer vision; deep learning; time-lapse