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Related Concept Videos

In Vitro Fertilization01:24

In Vitro Fertilization

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In vitro fertilization (IVF) is a form of assisted reproductive technology where an egg is fertilized with sperm in a controlled laboratory environment before transferring the resulting embryo into the uterus. This process is designed to help individuals and couples experiencing difficulties conceiving.
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Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
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EmbryoProfiler: A Visual Clinical Decision Support System for IVF.

Johannes Knittel, Simon Warchol, Robert Kruger

    IEEE Transactions on Visualization and Computer Graphics
    |December 5, 2025
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    Summary
    This summary is machine-generated.

    EmbryoProfiler enhances in-vitro fertilization (IVF) by providing visual analytics for embryo assessment. This system aids clinicians in selecting viable embryos, aiming to improve IVF success rates.

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    Area of Science:

    • Reproductive medicine
    • Medical imaging analysis
    • Computational biology

    Background:

    • In-vitro fertilization (IVF) is a common treatment for infertility, yet success rates remain low at approximately 20% per cycle.
    • Embryo selection is crucial but challenging, with current automated methods lacking transparency and requiring extensive manual input.
    • Time-lapse microscopy offers data for computational analysis, but clinical validation of automated tools is difficult.

    Purpose of the Study:

    • To develop EmbryoProfiler, a visual analytics system for assessing embryo viability using time-lapse microscopy imagery.
    • To support clinicians in making more informed embryo selection decisions for improved IVF outcomes.
    • To integrate explainable AI for transparent and clinically relevant embryo scoring.

    Main Methods:

    • Collaborative development of a visual analytics system with embryologists, biologists, and machine learning experts.
    • Implementation of a deep learning pipeline for automated image annotation and extraction of clinically interpretable features.
    • Design of a semi-automatic, visualization-based workflow with interactive tools like cell-shape plots for embryo assessment.

    Main Results:

    • The system provides a workflow for fertilization assessment, developmental timing, and morphological inspection.
    • Interactive visualizations facilitate efficient analysis of embryo characteristics.
    • An explainable machine learning classifier offers transparent embryo viability scoring to predict live birth outcomes.

    Conclusions:

    • EmbryoProfiler enables clinicians to make better-informed embryo selection decisions.
    • The system's visual and explainable AI approach addresses limitations of existing automated methods.
    • This tool has the potential to improve clinical outcomes in IVF treatments.