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Lay summary The success of IVF has remained stagnant for a decade. The focus of a great deal of research is to improve on the current ~30% success rate of IVF. Artificial intelligence (AI), or machines that mimic human intelligence, has been gaining traction for its potential to improve outcomes in medicine, such as cancer diagnosis from medical images. In this commentary, we discuss whether AI has the potential to improve fertility outcomes in the IVF clinic. Based on existing research, we examine the potential of adopting AI within multiple facets of an IVF cycle, including egg/sperm and embryo selection, as well as formulation of an IVF treatment regimen. We discuss both the potential benefits and concerns of the patient and clinician in adopting AI in the clinic. We outline hurdles that need to be overcome prior to implementation. We conclude that AI has an important future in improving IVF success.

Unfertilised eggs (oocytes) release chemical biomarkers into the medium surrounding them. This provides an opportunity to monitor cell health and development during assisted reproductive processes if detected in a non-invasive manner. Here we report the measurement of pH using an optical fibre probe, OFP1, in 5 μL drops of culture medium containing single mouse cumulus oocyte complexes (COCs). This allowed for the detection of statistically significant differences in pH between COCs in culture medium with no additives and those incubated with either a chemical (cobalt chloride) or hormonal treatment (follicle stimulating hormone); both of which serve to induce the release of lactic acid into the medium immediately surrounding the COC. Importantly, OFP1 was shown to be cell-safe with no inherent cell toxicity or light-induced phototoxicity indicated by negative DNA damage staining. Pre-measurement photobleaching of the probe reduced fluorescence signal variability, providing improved measurement precision (0.01-0.05 pH units) compared to previous studies. This optical technology presents a promising platform for the measurement of pH and the detection of other extracellular biomarkers to assess cell health during assisted reproduction. Refereed/Peer-reviewed

This study examined the nature and accuracy of information available across online platforms for couples trying to conceive. A consumer simulation-based investigation of English websites and social media (Facebook, Twitter, Instagram) was undertaken using common search terms identified in a pilot study. Claims about fertility and pregnancy health were then extracted from the results and analysed thematically. The accuracy of each claim was assessed independently by six fertility and conception experts, rated on a scale of 1 (not factual) to 4 (highly factual), with scores collated to produce a median rating. Claims with a median score