Deep Learning for Embryo Evaluation Using Time-Lapse: A Systematic Review of Diagnostic Test Accuracy.

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Abstract

To investigate the accuracy of convolutional neural networks (CNN) models for the assessment of embryos using time-lapse (TL) monitor.A systematic search was conducted in PubMed and Web of Science databases from January 2016 to December 2022. The search strategy was carried out by using keywords and MeSH terms.Studies were included if reported the accuracy of CNN models for embryo evaluation using TL. The review was registered with PROSPERO, the prospective international register of systematic reviews (Identification number CRD42021275916).Two reviewer authors independently screened results using Covidence systematic review software (Veritas Health Innovation, Melbourne, Australia). The full-text article was reviewed when the studies met the inclusion criteria or in any uncertainty. Nonconsensus was resolved by a third reviewer. Risk of bias and applicability were evaluated using the QUADAS-2 tool and by the modified Joanna Briggs Institute (JBI) checklist.Following a systematic search of the literature, twenty-two studies were identified as eligible for inclusion. All studies were retrospective. A total of 522,516 images of 222,998 embryos were analyzed. Three main outcomes were evaluated: successful in vitro fertilization (IVF), blastocyst stage classification, and blastocyst quality, The majority of the studies reporting >80% accuracy, and some outperformed embryologists. Ten studies scored a high risk of bias, mostly due to patient bias.The application of AI in TL monitors has the potential for more efficient, accurate, and objective embryo evaluation. Models that examined blastocyst stage classification showed the best predictions. Models that predicted live birth had a low risk of bias, used the largest databases, and had external validation, heightens their relevance to clinical application. Our systematic review is limited by the high heterogeneity among studies. Researchers should share databases and make standardized reporting.This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.Copyright © 2023 Elsevier Inc. All rights reserved.