ArticleJournal of assisted reproduction and genetics2026
Machine learning-enabled prediction of ART pregnancy outcomes: a systematic review and meta-analysis.
Article in Journal of assisted reproduction and genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
12 authors.
Funding
Abstract
objectiveTo systematically evaluate the diagnostic accuracy and methodological quality of machine learning (ML) prediction models for pregnancy outcomes after assisted reproductive technology (ART).
methodsPubMed, 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, pooled sensitivity, specificity, diagnostic odds ratio (DOR), and summary receiver operating characteristic (SROC) curves were estimated using random-effects diagnostic meta-analysis. Risk of bias was assessed with PROBAST.
resultsTwenty studies were included in the systematic review, of which 14 contributed to the diagnostic meta-analysis. Overall risk of bias was low in 1 study (5.0%), high in 8 studies (40.0%), and unclear in 11 studies (55.0%). The pooled sensitivity was 0.737 (95% CI, 0.662-0.799) and the pooled specificity was 0.789 (95% CI, 0.709-0.851), with substantial heterogeneity (I
conclusionML models show moderate diagnostic accuracy for predicting ART pregnancy outcomes, but the evidence base is limited by substantial heterogeneity and frequent high or unclear risk of bias. Future studies should follow TRIPOD + AI and PROBAST-aligned standards, report calibration and clinical utility, and prioritize prospective multi-center external validation before clinical implementation. SYSTEMATIC REVIEW REGISTRATION: PROSPERO, CRD420251108846.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.