ivf_lbr_prediction: Machine Learning Prediction of Live Birth After IVF Using Adenomyosis Features

Björnsson, Ola, Sara Alson
2025 · doi:10.5281/zenodo.17715462 · W7106696093
other OA: green CC0
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⚙ AI-generated summary by claude@2026-07, 2026-07-09 ⓘ

XGBoost models incorporating ultrasound features of adenomyosis from MUSA criteria were developed to predict live birth rates after IVF.

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⚙ AI-generated deep summary by claude@2026-07, 2026-07-09 · read from full text ⓘ

The paper describes an XGBoost-based machine learning approach to predict live birth after IVF using ultrasound features of adenomyosis defined by the Morphological Uterus Sonographic Assessment (MUSA) criteria. It provides code for hyperparameter optimization with Optuna, cross-validation, model evaluation, and feature importance interpretation using SHAP values. The main limitation stated in the description is that the repository focuses on the predictive modeling workflow and software implementation rather than presenting clinical outcome findings in the text provided. This paper is centrally about adenomyosis — it specifically incorporates MUSA-based adenomyosis ultrasound features to predict live birth rates following IVF.

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Abstract

XGBoost-based machine learning models for predicting live birth rates following in vitro fertilization (IVF) treatment, incorporating ultrasound features of adenomyosis based on the Morphological Uterus Sonographic Assessment (MUSA) criteria. This repository contains code for hyperparameter optimization using Optuna, cross-validation, model evaluation, and feature importance analysis using SHAP values.
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Published November 25, 2025 | Version v1.0.0 Software Open ivf_lbr_prediction: Machine Learning Prediction of Live Birth After IVF Using Adenomyosis Features Authors/Creators - 1. Department of Energy Sciences, Faculty of Engineering, Lund University, Lund, Sweden - 2. Obstetric, Gynecological and Prenatal Ultrasound research, Department of Clinical Sciences, Malmö, Lund University, Sweden Description XGBoost-based machine learning models for predicting live birth rates following in vitro fertilization (IVF) treatment, incorporating ultrasound features of adenomyosis based on the Morphological Uterus Sonographic Assessment (MUSA) criteria. This repository contains code for hyperparameter optimization using Optuna, cross-validation, model evaluation, and feature importance analysis using SHAP values. Files F-Ursus/ivf_lbr_prediction-v1.0.0.zip Files (32.9 kB) | Name | Size | Download all | |---|---|---| | md5:b5fe8ff6176de76c56086b58e05bc152 | 32.9 kB | Preview Download | Additional details Related works - Is supplement to - Software: https://github.com/F-Ursus/ivf_lbr_prediction/tree/v1.0.0 (URL) Software - Repository URL - https://github.com/F-Ursus/ivf_lbr_prediction

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Outcome instruments

MUSA

Condition tags

adenomyosis

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last seen: 2026-06-04T00:00:01.174412+00:00
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