Download PDF
CC BY-NC-ND 4.0 · Semin Reprod Med 2024; 42(02): 112-129
DOI: 10.1055/s-0044-1791536
Review Article

Patient-Centric In Vitro Fertilization Prognostic Counseling Using Machine Learning for the Pragmatist

Mylene W.M. Yao
1   R&D Department, Univfy, Los Altos, California
,
Julian Jenkins
2   Jencap Consulting Ltd., Cardiff, United Kingdom
,
Elizabeth T. Nguyen
1   R&D Department, Univfy, Los Altos, California
,
Trevor Swanson
1   R&D Department, Univfy, Los Altos, California
,
Marco Menabrito
1   R&D Department, Univfy, Los Altos, California
› Author Affiliations Funding Each organization funded its own participation.
› Further Information
   Permissions and Reprints

Abstract

Although in vitro fertilization (IVF) has become an extremely effective treatment option for infertility, there is significant underutilization of IVF by patients who could benefit from such treatment. In order for patients to choose to consider IVF treatment when appropriate, it is critical for them to be provided with an accurate, understandable IVF prognosis. Machine learning (ML) can meet the challenge of personalized prognostication based on data available prior to treatment. The development, validation, and deployment of ML prognostic models and related patient counseling report delivery require specialized human and platform expertise. This review article takes a pragmatic approach to review relevant reports of IVF prognostic models and draws from extensive experience meeting patients' and providers' needs with the development of data and model pipelines to implement validated ML models at scale, at the point-of-care. Requirements of using ML-based IVF prognostics at point-of-care will be considered alongside clinical ML implementation factors critical for success. Finally, we discuss health, social, and economic objectives that may be achieved by leveraging combined human expertise and ML prognostics to expand fertility care access and advance health and social good.

Keywords

prognostic counseling - live birth probability - artificial intelligence - machine learning - precision medicine

Authorship Contribution Statement

M.W.M.Y.: writing—original draft, writing—review and editing, conceptualization; J.J.: writing—original draft, writing—review and editing, conceptualization; E.T.N., T.S., M.M.: writing—review and editing.


Attestation Statement

Not applicable.


Data Sharing Statement

Not applicable.




Publication History

Article published online:
08 October 2024

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Thieme Medical Publishers, Inc.
333 Seventh Avenue, 18th Floor, New York, NY 10001, USA