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In Vitro Fertilization

In Vitro Fertilization

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.
The IVF process begins with ovarian stimulation, during which reproductive endocrinologists prescribe hormonal medications to stimulate the ovaries to produce multiple eggs instead of the single...

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Updated: Jun 22, 2025

OP-IVM: Combining In vitro Maturation after Oocyte Retrieval with Gynecological Surgery

OP-IVM: Combining In vitro Maturation after Oocyte Retrieval with Gynecological Surgery

Published on: May 9, 2021

Improving IVF Utilization with Patient-Centric Artificial Intelligence-Machine Learning (AI/ML): A Retrospective

Mylene W M Yao¹, Elizabeth T Nguyen¹, Matthew G Retzloff²

¹Department of R&D, Univfy Inc., 117 Main Street, #139, Los Altos, CA 94022, USA.

Journal of Clinical Medicine

|June 27, 2024

Summary

This summary is machine-generated.

Machine learning prognostic reports significantly increased in vitro fertilization (IVF) utilization among new fertility patients. These data-driven tools enhance patient-provider counseling and treatment decisions for better IVF success rates.

Keywords:

AI/ML IVF IVF access IVF conversion IVF outcomes IVF utilization artificial intelligence (AI)fertility fertility access live birth outcomes machine learning (ML)prediction model prognostics tool

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Published on: January 27, 2023

Area of Science:

Reproductive Medicine
Artificial Intelligence in Healthcare
Health Services Research

Background:

In vitro fertilization (IVF) remains underutilized globally, despite its potential to help millions conceive.
Effective patient-provider counseling and data-driven treatment decisions are crucial for optimizing IVF outcomes.
Existing prognostic tools may lack global applicability or local adaptability.

Purpose of the Study:

To investigate the association between the use of machine learning, center-specific (MLCS) prognostic reports (Univfy®) and IVF utilization rates.
To assess how MLCS reports impact provider-patient counseling and subsequent treatment decisions.
To determine if MLCS report usage influences the conversion rates to intra-uterine insemination (IUI) and/or IVF.

Main Methods:

Retrospective cohort study of 24,238 new patient visits across seven fertility centers (2016-2022).
Analysis of Univfy® report usage and its correlation with first IUI and/or IVF conversion within 180 days, 360 days, and "Ever" of the new patient visit.
Statistical analysis using odds ratios (OR) to determine the association between report usage and IVF conversion.

Main Results:

Univfy® report usage was significantly associated with higher direct and total IVF conversion rates across all timeframes (180-day, 360-day, Ever).
Odds ratios for total IVF conversion ranged from 2.78 to 3.81 (p < 0.05), indicating a substantial increase with report usage.
Older age was identified as a minor independent predictor of IVF conversion among patients using the Univfy® report, after accounting for center-specific factors.

Conclusions:

Patient-centric, MLCS-based prognostic reports are linked to increased IVF utilization among new fertility patients.
These reports can facilitate more informed treatment decisions and improve counseling.
Further research is recommended to explore factors influencing treatment decisions and optimize patient-centric workflows using MLCS reports.