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Oocyte and embryo evaluation by AI and multi-spectral autofluorescence imaging: Livestock embryology needs to catch-up to clinical practice
ارزیابی تخمک و جنین توسط هوش مصنوعی و تصویربرداری خودکار فلورسانس چند طیفی: جنین شناسی دام باید به مراحل بالینی برسد-2020 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. Keywords: Embryo selection | Machine learning | Pregnancy establishment | Embryo metabolism | Morphokinetics |
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