Non-invasive prediction of human embryonic ploidy using artificial intelligence: a systematic review and meta-analysis

Abstract

Background: Embryonic ploidy is critical for the success of embryo transfer. Currently, preimplantation genetic testing for aneuploidy (PGT-A) is the gold standard for detecting ploidy abnormalities. However, PGT-A has several inherent limitations, including invasive biopsy, high economic burden, and ethical constraints. This paper provides the first comprehensive systematic review and meta-analysis of the performance of artificial intelligence (AI) algorithms using embryonic images for non-invasive prediction of embryonic ploidy. Methods: ), derived from Cochran's Q test. Predefined subgroup analyses and bivariate meta-regression were conducted to explore potential sources of heterogeneity. This study was registered with PROSPERO (CRD42024500409). Findings: Twenty eligible studies were identified, with twelve studies included in the meta-analysis. The pooled sensitivity, specificity, and area under the curve of AI for predicting embryonic euploidy were 0.71 (95% CI: 0.59-0.81), 0.75 (95% CI: 0.69-0.80), and 0.80 (95% CI: 0.76-0.83), respectively, based on a total of 6879 embryos (3110 euploid and 3769 aneuploid). Meta-regression and subgroup analyses identified the type of AI-driven decision support system, external validation, risk of bias, and year of publication as the primary contributors to the observed heterogeneity. There was no evidence of publication bias. Interpretation: Our findings indicate that AI algorithms exhibit promising performance in predicting embryonic euploidy based on embryonic imaging. Although the current AI models developed cannot entirely replace invasive methods for determining embryo ploidy, AI demonstrates promise as an auxiliary decision-making tool for embryo selection, particularly for individuals who are unable to undergo PGT-A. To enhance the quality of future research, it is essential to overcome the specific challenges and limitations associated with AI studies in reproductive medicine. Funding: This work was supported by the National Key R&D Program of China (2022YFC2702905), the Shengjing Freelance Researcher Plan of Shengjing Hospital and the 345 talent project of Shengjing Hospital.

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