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<p>Infertility affects millions of couples worldwide, and in vitro fertilization is a key therapeutic strategy for achieving parenthood. Despite advances, the first IVF attempt fails in ~60% of patients, highlighting the need for innovative solutions to improve clinical outcomes. Challenges include the limited ability to study embryo implantation, inadequate methods to test therapeutic drugs, and lack of metrics to evaluate implantation images. To address these issues, we developed <i>ImplantoMetrics</i>, a Fiji plugin for quantitative assessment of trophoblast invasion in combination with a 3D-in-vitro model. <i>ImplantoMetrics</i> uses Convolutional Neural Network and XGBoosting to accurately measure multidimensional expansion patterns. It allows quantitative evaluation of potential therapeutic interventions in vitro and enables a complex study of trophoblast invasion. Compared to manual methods, <i>ImplantoMetrics</i> is ~13-times faster and reduces errors through automation. Beyond implantation research, <i>ImplantoMetrics</i> offers a comprehensive tool to study spheroid invasion in different biological contexts, as e.g. demonstrated here for cancer research.</p>

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Multidimensional trophoblast invasion assessment by combining 3D in vitro modeling and deep learning analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月20日

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  • Ayberk Alp Gyunesh,
  • Marlene Rezk-Füreder,
  • Celine Kapper,
  • Gil Mor,
  • Omar Shebl,
  • Peter Oppelt,
  • Patrick Stelzl,
  • Barbara Arbeithuber

摘要

Infertility affects millions of couples worldwide, and in vitro fertilization is a key therapeutic strategy for achieving parenthood. Despite advances, the first IVF attempt fails in ~60% of patients, highlighting the need for innovative solutions to improve clinical outcomes. Challenges include the limited ability to study embryo implantation, inadequate methods to test therapeutic drugs, and lack of metrics to evaluate implantation images. To address these issues, we developed ImplantoMetrics, a Fiji plugin for quantitative assessment of trophoblast invasion in combination with a 3D-in-vitro model. ImplantoMetrics uses Convolutional Neural Network and XGBoosting to accurately measure multidimensional expansion patterns. It allows quantitative evaluation of potential therapeutic interventions in vitro and enables a complex study of trophoblast invasion. Compared to manual methods, ImplantoMetrics is ~13-times faster and reduces errors through automation. Beyond implantation research, ImplantoMetrics offers a comprehensive tool to study spheroid invasion in different biological contexts, as e.g. demonstrated here for cancer research.