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Updated: Jan 12, 2026

Minimally Invasive Embryo Transfer and Embryo Vitrification at the Optimal Embryo Stage in Rabbit Model

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Innovations in assisted reproductive technology through the advances in artificial intelligence and image processing.

¹Department of Obstetrics and Gynecology, SMG-SNU Boramae Medical Center, Seoul, Korea.

Obstetrics & Gynecology Science

|November 7, 2025

Summary

This summary is machine-generated.

Artificial intelligence (AI) and advanced imaging enhance assisted reproductive technology (ART) by improving embryo selection and ovarian monitoring. These innovations offer objective, reproducible assessments for better reproductive medicine outcomes.

Keywords:

Artificial intelligence Embryo In vitro fertilization Reproductive medicine Time-lapse imaging

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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Area of Science:

Reproductive Medicine
Biomedical Imaging
Artificial Intelligence

Background:

Traditional assisted reproductive technology (ART) methods for embryo selection and ovarian monitoring rely on subjective interpretation, leading to variability.
Artificial intelligence (AI) offers enhanced objectivity, consistency, and reproducibility in clinical decisions within reproductive medicine.

Purpose of the Study:

To explore the integration of AI and advanced imaging technologies in reproductive medicine.
To highlight the potential of AI and fluorescence lifetime imaging microscopy for objective embryo and follicle evaluation.
To discuss the application of AI in self-operated reproductive health monitoring.

Main Methods:

Utilizing AI-enhanced models for consistent performance in embryo selection and monitoring.
Employing fluorescence lifetime imaging microscopy for real-time, label-free metabolic assessment of gametes and embryos.
Developing AI-powered self-assessment tools for ovarian follicles using smartphone-compatible ultrasound devices.

Main Results:

AI models demonstrate reduced inter- and intra-observer variability compared to traditional methods.
Metabolic signatures from fluorescence lifetime imaging correlate with embryo developmental competence, offering non-invasive quality assessment.
AI facilitates automated follicle segmentation and self-assessment, improving ovarian stimulation tracking and patient engagement.

Conclusions:

The integration of AI, advanced image processing, and metabolic imaging represents a significant advancement in reproductive medicine.
These technologies enhance the precision of embryo and follicle evaluation.
Multimodal platforms combining clinical, morphological, and metabolic data hold promise for optimizing ART outcomes.