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of such data , AI has been developed — through deep learning — to maximise the use of images collected during development and thereby reduce the subjective interoperator variation inherent between embryologists . 11 , 12 AI also significantly reduces the time taken to assess the embryos of a patient from several minutes , when performed by an embryologist , down to just a few seconds .
Although assessment of embryo morphology is related to transfer outcomes , such an approach does not incorporate data on the actual physiology of the embryo itself . Metabolic activity of the human embryo , as quantitated completely non-invasively by analysis of spent culture media , has been shown to be related to transfer outcomes . 13-15 Advances in this area may also come from the use of microfluidic devices and the introduction of novel microscopies , which can indirectly
assess the metabolic state of embryos and may even be able to differentiate euploid from aneuploid cells . 16-19
As more data are collected per embryo as it develops , it is expected that AI will
be further used to create selection algorithms using both morphological data and biomarkers of embryonic health , such as metabolism . 8 , 20
AUTOMATION Microrobotics is another technology that has been evaluated to standardise techniques such as ICSI . Recently , the birth of the first babies born following the use of a robotic system to perform IVF ( controlled by engineers and not embryologists ) has been reported . 23 When such an approach is combined with specifically designed
There is a good chance that , within the next few years , several manual aspects of IVF will become partly or fully automated .
and microfabricated devices and the integration of AI , there is a good chance that , within the next few years , several manual aspects of IVF will become partly or fully automated . 7
ARTIFICIAL INTELLIGENCE In addition to the aforementioned examples , AI also has the capacity to increase the overall functioning of a complex IVF laboratory through real-time monitoring of the key performance indicators of success , providing early detection of any issues within the IVF laboratory before they can affect patient outcomes . 24 Further , AI can be used to assist the clinician in optimising patient stimulation regimens . 25
Conclusion
Clinical evaluation by AI and microrobotic , microfluidic and microfabricated devices in human IVF have commenced . It is envisaged that these technologies will not only standardise several of the technically demanding aspects of humanassisted conception but should also lead to increases in success and availability of infertility treatment . 8 , 26 References on request from kate . kelso @ adg . com . au
Several new technologies are being developed and evaluated to improve both the efficiency and efficacy of IVF .