Artificial intelligence in time-lapse system: advances, applications, and future perspectives in reproductive medicine

Thi My Trang Luong, Nguyen Quoc Khanh Le

Research output: Contribution to journalReview articlepeer-review

Abstract

With the rising demand for in vitro fertilization (IVF) cycles, there is a growing need for innovative techniques to optimize procedure outcomes. One such technique is time-lapse system (TLS) for embryo incubation, which minimizes environmental changes in the embryo culture process. TLS also significantly advances predicting embryo quality, a crucial determinant of IVF cycle success. However, the current subjective nature of embryo assessments is due to inter- and intra-observer subjectivity, resulting in highly variable results. To address this challenge, reproductive medicine has gradually turned to artificial intelligence (AI) to establish a standardized and objective approach, aiming to achieve higher success rates. Extensive research is underway investigating the utilization of AI in TLS to predict multiple outcomes. These studies explore the application of popular AI algorithms, their specific implementations, and the achieved advancements in TLS. This review aims to provide an overview of the advances in AI algorithms and their particular applications within the context of TLS and the potential challenges and opportunities for further advancements in reproductive medicine.

Original languageEnglish
Pages (from-to)239-252
Number of pages14
JournalJournal of Assisted Reproduction and Genetics
Volume41
Issue number2
DOIs
Publication statusPublished - Feb 2024

Keywords

  • Artificial intelligence
  • Assisted reproductive technology
  • In vitro fertilization
  • Medical imaging
  • Neural networks
  • Time-lapse system

ASJC Scopus subject areas

  • Reproductive Medicine
  • Genetics
  • Obstetrics and Gynaecology
  • Developmental Biology
  • Genetics(clinical)

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