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Machine learning models for IVF live-birth prediction show limited gains over simpler methods

By PulseAugur Editorial · [1 sources] ·

A systematic review and meta-analysis of machine learning models for predicting live birth after IVF/ICSI found that while reported performance varies widely, the actual gains over simpler logistic regression models are modest. The review, which searched databases up to October 2026, identified 48 studies but found that most were at high risk of bias, with limited assessment of model calibration and no evidence of clinical impact. The researchers suggest that improvements in prediction stem more from staging predictions and local calibration rather than complex model architectures. AI

IMPACT Suggests that complex ML models offer minimal advantage over simpler methods for IVF live-birth prediction, highlighting issues with validation and clinical impact.

RANK_REASON The item is a research paper detailing a systematic review and meta-analysis of existing models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Machine learning models for IVF live-birth prediction show limited gains over simpler methods

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The item is a research paper detailing a systematic review and meta-analysis of existing models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Abdul Raheem Shahzad, Muhammad Zaheer Khan, Umar Ajaib, Asad Ur Rehman, Juan Deng, Ming Zhang ·

    Machine Learning for Live-Birth Prediction After IVF/ICSI From Structured Clinical Data: A Systematic Review, Meta-Analysis, and Critical Appraisal

    arXiv:2611.07124v1 Announce Type: new Abstract: Machine-learning models predicting live birth after in vitro fertilisation (IVF) or intracytoplasmic sperm injection (ICSI) from routine clinical data report performance from near chance to near perfect. We systematically reviewed t…

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