aceMachine learning-enabled prediction of ART pregnancy outcomes: a systematic review and meta-analysis
Objective To systematically evaluate the diagnostic accuracy and methodological quality of machine learning (ML) prediction models for pregnancy outcomes after assisted reproductive technology (ART). Methods PubMed, Embase, the Cochrane Library, IEEE Xplore, MEDLINE, ClinicalTrials.gov, CNKI, Wanfang, and VIP were searched from inception to July 2026. Eligible studies developed or validated ML models to predict clinical pregnancy or live birth after ART. For studies reporting complete 2 × 2 contingency data, poo…
Machine learning models achieved moderate diagnostic accuracy for predicting clinical pregnancy or live birth after assisted reproductive technology, with pooled sensitivity 0.737 and specificity 0.789.
The evidence base for ML prediction of ART outcomes is limited by substantial heterogeneity and frequent high or unclear risk of bias, requiring prospective multi-center external validation before clinical use.
Evidence base limited by substantial heterogeneity (I2 97.7% and 99.0%) and frequent high or unclear risk of bias, with subgroup findings driven by a single prospective study.
Evidence
- Peer-reviewedJournal of Assisted Reproduction and Genetics2026-08-06
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Truvace Impact Record TRV-2026-0674, v1: “Machine learning-enabled prediction of ART pregnancy outcomes: a systematic review and meta-analysis.” Truvace, 2026-08-07. /record/TRV-2026-0674 (accessed at citation time). sha256 b9126d239081962f…
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