Enhancing Predictive Accuracy in Embryo Implantation: The Bonna Algorithm and its Clinical Implications

Gilad Rave, Daniel E. Fordham, Alex M. Bronstein, David H. Silver

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In the context of in vitro fertilization (IVF), selecting embryos for transfer is critical in determining pregnancy outcomes, with implantation as the essential first milestone for a successful pregnancy. This study introduces the Bonna algorithm, an advanced deep-learning framework engineered to predict embryo implantation probabilities. The algorithm employs a sophisticated integration of machine-learning techniques, utilizing MobileNetV2 for pixel and context embedding, a custom Pix2Pix model for precise segmentation, and a Vision Transformer for additional depth in embedding. MobileNetV2 was chosen for its robust feature extraction capabilities, focusing on textures and edges. The custom Pix2Pix model is adapted for precise segmentation of significant biological features such as the zona pellucida and blastocyst cavity. The Vision Transformer adds a global perspective, capturing complex patterns not apparent in local image segments. Tested on a dataset of images of human blastocysts collected from Ukraine, Israel, and Spain, the Bonna algorithm was rigorously validated through 10-fold cross-validation to ensure its robustness and reliability. It demonstrates superior performance with a mean area under the receiver operating characteristic curve (AUC) of 0.754, significantly outperforming existing models. The study not only advances predictive accuracy in embryo selection but also highlights the algorithm’s clinical applicability due to reliable confidence reporting.

Original languageEnglish
Title of host publicationArtificial Intelligence in Healthcare - 1st International Conference, AIiH 2024, Proceedings
EditorsXianghua Xie, Gibin Powathil, Iain Styles, Marco Ceccarelli
PublisherSpringer Science and Business Media Deutschland GmbH
Pages160-171
Number of pages12
ISBN (Print)9783031672842
DOIs
StatePublished - 2024
Event1st International Conference on Artificial Intelligence in Healthcare, AIiH 2024 - Swansea, United Kingdom
Duration: 4 Sep 20246 Sep 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14976 LNCS

Conference

Conference1st International Conference on Artificial Intelligence in Healthcare, AIiH 2024
Country/TerritoryUnited Kingdom
CitySwansea
Period4/09/246/09/24

Keywords

  • Artificial Intelligence in Reproductive Medicine
  • Clinical Decision Support
  • Deep Learning
  • Embryo Implantation
  • Predictive Modeling

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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