# A prediction algorithm for optimal number of oocytes to fertilize during in vitro fertilization treatment

> **NIH NIH R03** · AMHERST COLLEGE · 2021 · $65,150

## Abstract

Project Summary/Abstract
In vitro fertilization (IVF) treatment has become increasingly more common and
successful over the last decade. Extra frozen embryos (embryos that couples do not
use right away, but continue to store) pose an emotional and financial dilemma to
patients. The extra embryos also lead to a logistical and financial dilemma for clinics.
One strategy to limit the number of extra embryos in storage is to limit the number of
embryos created in the first place. However, there currently is no validated method to
determine how many eggs should be fertilized during in vitro fertilization treatment such
that enough embryos are formed to have the desired number of children while
minimizing the number of extra embryos.
Our objective is to develop a prediction tool to aid clinicians and patients in deciding
how many eggs should be fertilized during IVF. In Specific Aim 1, we will develop an
algorithm using existing data from the national Society for Assisted Reproductive
Technology Clinical Outcome Reporting System (SART CORS) database. We propose
to develop two separate models, one to predict the number of embryos that will need to
be transferred to yield one live birth and the second to predict the proportion of fertilized
eggs that will yield transferrable embryos. Both models will consider patient
demographics and pre-retrieval cycle characteristics as possible predictors. The final
algorithm will involve a function of the ratio of the two predictions. In Specific Aim 2, we
will survey IVF patients and providers to assess their interest and perspective in utilizing
the prediction tool. Survey responses will help elucidate possible barriers to utilization,
informing us on how best to educate patients and providers on the value of the
prediction tool. This proposal is of high importance because it has the potential to
change the way IVF is conducted, thus limiting the number of eggs that are fertilized
and creating less embryos. Having less embryos will minimize the emotional, financial,
and logistical tolls that extra embryos pose to patients and providers. With successful
completion of the proposed aims, we will have a tool that can help promote safer,
effective, more responsible care for IVF patients.

## Key facts

- **NIH application ID:** 10218608
- **Project number:** 1R03HD102518-01A1
- **Recipient organization:** AMHERST COLLEGE
- **Principal Investigator:** Katharine Fischer Berry Correia
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $65,150
- **Award type:** 1
- **Project period:** 2021-03-15 → 2023-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10218608

## Citation

> US National Institutes of Health, RePORTER application 10218608, A prediction algorithm for optimal number of oocytes to fertilize during in vitro fertilization treatment (1R03HD102518-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10218608. Licensed CC0.

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