Does artificial intelligence have a role in the IVF clinic?

Funding: K R D is supported by a Mid-Career Fellowship from the Hospital Research Foundation (C-MCF-58-2019). K D is supported by the UK Engineering and Physical Sciences Research Council (grants EP/P030017/1 and EP/R004854/1).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.Publisher PDFPeer reviewe

Experimentally unsupervised deconvolution for light-sheet microscopy with propagation-invariant beams

This project was funded by the UK Engineering and Physical Sciences Research Council (grants EP/P030017/1 and EP/R004854/1), and has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement (EC-GA 871212) and H2020 FETOPEN project "Dynamic” (EC-GA 863203). P.W. was supported by the 1851 Research Fellowship from the Royal Commission. KRD was supported by a Mid-Career Fellowship from the Hospital Research Foundation (C-MCF-58-2019). K.D. acknowledges support from the Australian Research Council through a Laureate Fellowship. S.S. was funded by BBSRC (BB/M00905X/1).Deconvolution is a challenging inverse problem, particularly in techniques that employ complex engineered point-spread functions, such as microscopy with propagation-invariant beams. Here, we present a deep-learning method for deconvolution that, in lieu of end-to-end training with ground truths, is trained using known physics of the imaging system. Specifically, we train a generative adversarial network with images generated with the known point-spread function of the system, and combine this with unpaired experimental data that preserve perceptual content. Our method rapidly and robustly deconvolves and super-resolves microscopy images, demonstrating a two-fold improvement in image contrast to conventional deconvolution methods. In contrast to common end-to-end networks that often require 1000–10,000s paired images, our method is experimentally unsupervised and can be trained solely on a few hundred regions of interest. We demonstrate its performance on light-sheet microscopy with propagation-invariant Airy beams in oocytes, preimplantation embryos and excised brain tissue, as well as illustrate its utility for Bessel-beam LSM. This method aims to democratise learned methods for deconvolution, as it does not require data acquisition outwith the conventional imaging protocol.Publisher PDFPeer reviewe

Vitrification within a nanoliter volume : oocyte and embryo cryopreservation within a 3D photopolymerized device

Open Access funding enabled and organized by CAUL and its Member Institutions. KRD is supported by a Mid-Career Fellowship from the Hospital Research Foundation (C-MCF-58–2019). KD acknowledges funding from the UK Engineering and Physical Sciences Research Council (grants EP/P030017/1). This study was funded by the Australian Research Council (ARC) Centre of Excellence for Nanoscale BioPhotonics (CE140100003).Purpose Vitrification permits long-term banking of oocytes and embryos. It is a technically challenging procedure requiring direct handling and movement of cells between potentially cytotoxic cryoprotectant solutions. Variation in adherence to timing, and ability to trace cells during the procedure, affects survival post-warming. We hypothesized that minimizing direct handling will simplify the procedure and improve traceability. To address this, we present a novel photopolymerized device that houses the sample during vitrification. Methods The fabricated device consisted of two components: the Pod and Garage. Single mouse oocytes or embryos were housed in a Pod, with multiple Pods docked into a Garage. The suitability of the device for cryogenic application was assessed by repeated vitrification and warming cycles. Oocytes or early blastocyst-stage embryos were vitrified either using standard practice or within Pods and a Garage and compared to non-vitrified control groups. Post-warming, we assessed survival rate, oocyte developmental potential (fertilization and subsequent development) and metabolism (autofluorescence). Results Vitrification within the device occurred within ~ 3 nL of cryoprotectant: this volume being ~ 1000-fold lower than standard vitrification. Compared to standard practice, vitrification and warming within our device showed no differences in viability, developmental competency, or metabolism for oocytes and embryos. The device housed the sample during processing, which improved traceability and minimized handling. Interestingly, vitrification-warming itself, altered oocyte and embryo metabolism. Conclusion The Pod and Garage system minimized the volume of cryoprotectant at vitrification—by ~ 1000-fold—improved traceability and reduced direct handling of the sample. This is a major step in simplifying the procedure.Publisher PDFPeer reviewe

The effect of discrete wavelengths of visible light on the developing murine embryo

Open Access funding enabled and organized by CAUL and its Member Institutions KRD is supported by a Mid-Career Fellowship from the Hospital Research Foundation (C-MCF-58–2019). KD is supported by funding from the UK Engineering and Physical Sciences Research Council (EP/P030017/1) and the Australian Research Council (FL210100099). CC acknowledges the support of a PhD scholarship jointly from the University of Adelaide and University of Nottingham. This study was funded by the Australian Research Council Centre of Excellence for Nanoscale BioPhotonics (CE140100003). PR acknowledges funding through the RMIT Vice-Chancellor’s Research Fellowship and ARC DECRA Fellowship scheme (DE200100279).Purpose A current focus of the IVF field is non-invasive imaging of the embryo to quantify developmental potential. Such approaches use varying wavelengths to gain maximum biological information. The impact of irradiating the developing embryo with discrete wavelengths of light is not fully understood. Here, we assess the impact of a range of wavelengths on the developing embryo. Methods Murine preimplantation embryos were exposed daily to wavelengths within the blue, green, yellow, and red spectral bands and compared to an unexposed control group. Development to blastocyst, DNA damage, and cell number/allocation to blastocyst cell lineages were assessed. For the longer wavelengths (yellow and red), pregnancy/fetal outcomes and the abundance of intracellular lipid were investigated. Results Significantly fewer embryos developed to the blastocyst stage when exposed to the yellow wavelength. Elevated DNA damage was observed within embryos exposed to blue, green, or red wavelengths. There was no effect on blastocyst cell number/lineage allocation for all wavelengths except red, where there was a significant decrease in total cell number. Pregnancy rate was significantly reduced when embryos were irradiated with the red wavelength. Weight at weaning was significantly higher when embryos were exposed to yellow or red wavelengths. Lipid abundance was significantly elevated following exposure to the yellow wavelength. Conclusion Our results demonstrate that the impact of light is wavelength-specific, with longer wavelengths also impacting the embryo. We also show that effects are energy-dependent. This data shows that damage is multifaceted and developmental rate alone may not fully reflect the impact of light exposure.Publisher PDFPeer reviewe

Hypofibrinolysis in diabetes: a therapeutic target for the reduction of cardiovascular risk

An enhanced thrombotic environment and premature atherosclerosis are key factors for the increased cardiovascular risk in diabetes. The occlusive vascular thrombus, formed secondary to interactions between platelets and coagulation proteins, is composed of a skeleton of fibrin fibres with cellular elements embedded in this network. Diabetes is characterised by quantitative and qualitative changes in coagulation proteins, which collectively increase resistance to fibrinolysis, consequently augmenting thrombosis risk. Current long-term therapies to prevent arterial occlusion in diabetes are focussed on anti-platelet agents, a strategy that fails to address the contribution of coagulation proteins to the enhanced thrombotic milieu. Moreover, antiplatelet treatment is associated with bleeding complications, particularly with newer agents and more aggressive combination therapies, questioning the safety of this approach. Therefore, to safely control thrombosis risk in diabetes, an alternative approach is required with the fibrin network representing a credible therapeutic target. In the current review, we address diabetes-specific mechanistic pathways responsible for hypofibrinolysis including the role of clot structure, defects in the fibrinolytic system and increased incorporation of anti-fibrinolytic proteins into the clot. Future anti-thrombotic therapeutic options are discussed with special emphasis on the potential advantages of modulating incorporation of the anti-fibrinolytic proteins into fibrin networks. This latter approach carries theoretical advantages, including specificity for diabetes, ability to target a particular protein with a possible favourable risk of bleeding. The development of alternative treatment strategies to better control residual thrombosis risk in diabetes will help to reduce vascular events, which remain the main cause of mortality in this condition

Does artificial intelligence have a role in the IVF clinic?

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