Metabolic imaging for gamete and embryo assessment through advanced microscopy technologies: a novel avenue for artificial intelligence?

Abstract

Infertility affects approximately one in six people globally and demand for ARTs, is expected to rise as parenthood is increasingly delayed. However, ART success remains constrained by gamete and embryo quality, female age, and biological factors beyond chromosomal status alone, highlighting the need for non-invasive methods that can complement current morphology-based and genetic approaches. Because gamete and embryo developmental competence is tightly coupled to cellular metabolism, label-free metabolic imaging has emerged as a promising strategy to assess developmental potential through endogenous autofluorescence of reduced nicotinamide adenine dinucleotide/phosphate [NAD(P)H] and oxidized flavins (FAD), which provide optical proxies of redox balance, mitochondrial activity, and oxidative metabolism. This invited mini-review synthesizes the biochemical basis of NAD(P)H/FAD autofluorescence signals, relates these readouts to the unique metabolic programs of oocytes, embryos across preimplantation development, and sperm, and reviews reproductive studies using fluorescence lifetime imaging microscopy, hyperspectral microscopy, and emerging light-sheet fluorescence microscopy. We discuss practical and interpretive challenges, including modality-dependent signal biases and recent consensus efforts towards standardization. Evidence linking metabolic signatures to reproductive ageing, developmental potential, and embryo ploidy status suggests that metabolic imaging, particularly when paired with artificial intelligence (AI), could enable automated, objective decision support for gamete and embryo selection. Finally, we briefly outline how AI could convert complex metabolic imaging data into clinically interpretable decision-support outputs for embryo ranking and risk stratification. Advances in rapid volumetric imaging, microsystems, and AI may support future ART workflows aimed at improving efficiency, accessibility, and cost-effectiveness.