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From Womb to World

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Bridging Fertility to

Family Journey

Our aim is to revolutionise the fertility to family journey, providing a comprehensive pipeline that images and annotates patient data using artificial intelligence.

 

 

 

 

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Computer vision approaches can create a lasting memory for parents-to-be, as well as another source of annotated data for medical records. These can be analysed by researchers and referred to later by clinicians to understand if there is promising predictive value for future generations.

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Through our "informed imaging" interface, developed with the Lister Hospital in London, we hope to bridge the gap between health care providers, parents to be, cutting edge researchers predicting fertility and child outcomes from earlier data. Our interface will clarify the deluge of medical results parents-to-be receive during their fertility journey.

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Computer vision approaches can create a lasting memory for parents-to-be, as well as another source of annotated data for medical records. These can be analysed by researchers and referred to later by clinicians to understand if there is promising predictive value for future generations.

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We hope our informed imaging interface can facilitate first glimpses of the emerging child, by extracting important details from images of the parents own follicles to implanted embryo to the interactive face of the fetus.

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If parents continue to contribute to the platform after their baby arrives, we can even return annotate videos of newborns to identify behaviours and moments for bonding or tag the motor features of early milestones.

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Preparing parents to experience these first moments through an informed lens can lay the foundations for an enduring bond between mother and baby.

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AI Research for Future Families

We use computer vision to annotate the medical imaging data: we hope these annotations will serve as a visual roadmap for parents-to-be, supplementing the appointments with fertility specialist and sonographer's careful medical expertise that walks each patient through their imaging.

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By displaying to parents what information the computer vision algorithm identified as important to classifying a pregnancy's trajectory or motor features of fetal and infant movements, we hope to make plain the data driving fertility decision-making.

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