Comparative analysis of convolutional neural networks and traditional machine learning models for IVF live birth prediction: a retrospective analysis of 48514 IVF cycles and an evaluation of deployment feasibility in resource-constrained settings

Comparative analysis of convolutional neural networks and traditional machine learning models for IVF live birth prediction: a retrospective analysis of 48514 IVF cycles and an evaluation of deployment feasibility in resource-constrained settings

ObjectiveTo evaluate the predictive performance of a convolutional neural network for analyzing electronic medical records in assisted reproductive therapy and to compare its accuracy and interpretability with traditional machine learning models. The study also explores the feasibility of deploying...

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Bibliographic Details
Main Authors: Yu Liu, Yi Wang, Kai Huang, Hao Shi, Hang Xin, Shanjun Dai, Jinhao Liu, Xinhong Yang, Jianyuan Song, Fuli Zhang, Yihong Guo
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-06-01
Series:Frontiers in Endocrinology
Subjects:
assisted reproductive technology
machine learning
deep learning
convolutional neural network
artificial intelligence
resource-limited settings
Online Access:https://www.frontiersin.org/articles/10.3389/fendo.2025.1556681/full
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https://www.frontiersin.org/articles/10.3389/fendo.2025.1556681/full

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