Artificial intelligence model to predict pregnancy and multiple pregnancy risk following in vitro fertilization-embryo transfer (IVF-ET)

Wen, Jen-Yu; Liu, Chung-Fen; Chung, Ming-Ting; Tsai, Yao‐Chou

doi:10.1016/j.tjog.2021.11.038

Cited by 13 publications

(1 citation statement)

References 15 publications

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“…Finally, some IVF prediction models such as that reported by Wen et al, 2022 contributed to our insights but would not practically be usable for patient counseling in the pre-treatment context, since they required data that are not available prior to starting IVF (e.g., number of oocytes, number of blastocysts, etc.) [ 32 ]. A discussion of the similarities and differences of the above-mentioned IVF pre-treatment models necessarily relate to model design, training techniques, validation, and performance metrics, which are beyond the scope of this article but are provided in a review by Yao et al, submitted [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

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

Yao,

Nguyen,

Retzloff

et al. 2024

JCM

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Objectives: In vitro fertilization (IVF) has the potential to give babies to millions more people globally, yet it continues to be underutilized. We established a globally applicable and locally adaptable IVF prognostics report and framework to support patient–provider counseling and enable validated, data-driven treatment decisions. This study investigates the IVF utilization rates associated with the usage of machine learning, center-specific (MLCS) prognostic reports (the Univfy® report) in provider-patient pre-treatment and IVF counseling. Methods: We used a retrospective cohort comprising 24,238 patients with new patient visits (NPV) from 2016 to 2022 across seven fertility centers in 17 locations in seven US states and Ontario, Canada. We tested the association of Univfy report usage and first intra-uterine insemination (IUI) and/or first IVF usage (a.k.a. conversion) within 180 days, 360 days, and “Ever” of NPV as primary outcomes. Results: Univfy report usage was associated with higher direct IVF conversion (without prior IUI), with odds ratios (OR) 3.13 (95% CI 2.83, 3.46), 2.89 (95% CI 2.63, 3.17), and 2.04 (95% CI 1.90, 2.20) and total IVF conversion (with or without prior IUI), OR 3.41 (95% CI 3.09, 3.75), 3.81 (95% CI 3.49, 4.16), and 2.78 (95% CI 2.59, 2.98) in 180-day, 360-day, and Ever analyses, respectively; p < 0.05. Among patients with Univfy report usage, after accounting for center as a factor, older age was a small yet independent predictor of IVF conversion. Conclusions: Usage of a patient-centric, MLCS-based prognostics report was associated with increased IVF conversion among new fertility patients. Further research to study factors influencing treatment decision making and real-world optimization of patient-centric workflows utilizing the MLCS reports is warranted.

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Section: Introductionmentioning

confidence: 99%