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Related Experiment Video

Updated: May 9, 2026

Mouse Oocyte Microinjection, Maturation and Ploidy Assessment

Mouse Oocyte Microinjection, Maturation and Ploidy Assessment

Published on: July 23, 2011

Optimizing oocyte yield utilizing a machine learning model for dose and trigger decisions, a multi-center,

Chelsea Canon¹, Lily Leibner¹, Michael Fanton²

¹RMA of New York, 635 Madison Avenue, 10th Floor, New York, NY, 10022, USA.

Scientific Reports

|August 20, 2024

Summary

This summary is machine-generated.

Artificial intelligence (AI) assisted clinicians in optimizing follicle stimulating hormone (FSH) dosage and trigger timing for in vitro fertilization (IVF). While not statistically significant, AI use showed trends toward improved outcomes and reduced FSH consumption in IVF cycles.

Keywords:

Artificial intelligence Clinical study Embryo selection Machine learning

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Published on: September 3, 2021

Area of Science:

Reproductive Medicine
Clinical Trials
Artificial Intelligence in Healthcare

Background:

In vitro fertilization (IVF) treatment relies on precise hormonal stimulation and timing for optimal outcomes.
Determining the ideal starting dose of follicle stimulating hormone (FSH) and trigger injection timing can be complex.
Artificial intelligence (AI) offers potential for data-driven decision support in clinical workflows.

Purpose of the Study:

To evaluate the clinical outcomes of IVF patients when clinicians utilized an AI platform.
To assess AI's impact on determining the optimal starting dose of FSH and trigger injection timing.
To compare patient outcomes between AI-assisted and conventional IVF treatment protocols.

Main Methods:

A prospective clinical trial with a historical control arm was conducted.
Four physicians across two US fertility centers participated.
The treatment group (N=291) used AI for FSH dose and trigger timing; the control group received standard care.
Key outcome measures included total FSH used and the average number of mature metaphase II (MII) oocytes retrieved.

Main Results:

The AI-assisted group showed a trend towards improved outcomes, with an average of 12.20 MII oocytes versus 11.24 in the control group (p=0.16).
Average oocytes retrieved were 16.01 (AI group) vs. 14.54 (control group), indicating a potential benefit (p=0.08).
The AI group used less total FSH (3671.95 IUs) compared to the control group (3846.29 IUs), a difference of -174.35 IUs (p=0.13).

Conclusions:

AI can safely assist clinicians in refining FSH starting doses and trigger injection timing during ovarian stimulation.
Physician use of AI in IVF demonstrates a potential to optimize the number of MII oocytes retrieved.
Further research may confirm AI's role in enhancing IVF treatment efficiency and patient outcomes.