Oocyte and embryo evaluation by AI and multi-spectral auto-fluorescence imaging: Livestock embryology needs to catch-up to clinical practice

• Published in: Theriogenology 2020-07, Vol.150, p.255-262
• Main Authors: McLennan, H.J., Saini, A., Dunning, K.R., Thompson, J.G.
• Format: Article
• Language: English
• Subjects: Animals; Embryo Culture Techniques; Embryo metabolism; Embryo selection; Embryo, Mammalian - physiology; Embryonic Development - physiology; Humans; Livestock - embryology; Machine learning; Mammals; Morphokinetics; Optical Imaging - methods; Optical Imaging - veterinary; Pregnancy establishment
• ISSN: 0093-691X; 1879-3231
• DOI: 10.1016/j.theriogenology.2020.01.061

A highly accurate ‘non-invasive quantitative embryo assessment for pregnancy’ (NQEAP) technique that determines embryo quality has been an elusive goal. If developed, NQEAP would transform the selection of embryos from both Multiple Ovulation and Embryo Transfer (MOET), and even more so, in vitro produced (IVP) embryos for livestock breeding. The area where this concept is already having impact is in the field of clinical embryology, where great strides have been taken in the application of morphokinetics and artificial intelligence (AI); while both are already in practice, rigorous and robust evidence of efficacy is still required. Even the translation of advances in the qualitative scoring of human IVF embryos have yet to be translated to the livestock IVP industry, which remains dependent on the MOET-standardised 3-point scoring system. Furthermore, there are new ways to interrogate the biochemistry of individual embryonic cells by using new, light-based methodologies, such as FLIM and hyperspectral microscopy. Combinations of these technologies, in particular combining new imaging systems with AI, will lead to very accurate NQEAP predictive tools, improving embryo selection and recipient pregnancy success. •Cattle IVP embryo assessment development is poor relative to human IVF.•IVP embryos need better defined morphological scoring systems.•Non-invasive quantitative human embryo assessment for pregnancy advancing rapidly.•Auto-fluorescence assessment of embryo metabolism advancing.•Imaging and machine-learning algorithms of embryo quality will be developed together.