Fertility
New Visa programme could boost femtech founders amid investment gap

Femtech startups may be able to access a new investment scheme from Visa, which aims to close the funding gap experienced by female founders.
Visa Foundaton has launched the CatalyseHer Programme in the UK and is aiming to provide 500 women entrepreneurs with training and fundraising support to grow their businesses and make them more attractive to investors.
The programme includes training, networking and micro-grants and is aimed at early stage, women-owned businesses.
It is seeking to 500 UK-based female founders to address the gender financing gap facing women entrepreneurs in the UK.
Women entrepreneurs still face barriers when it comes to access to the investments and resources that support innovation.
Women-founded businesses in the UK receive only a two per cent share of total venture capital investments according to the British Business Bank.
Najada Kumbuli, head of investments and partnerships, Visa Foundation said: “Women entrepreneurs play a crucial role in all economies by driving innovation and impact at scale.
“This programme aims to address the main challenges faced by women entrepreneurs and create a ripple effect across many communities in the UK.”
Mandy Lamb, MD in the UK and Ireland at Visa, said: “Women entrepreneurs are critical in developing a more sustainable, equitable future. By addressing barriers and providing access to resources such as funding, mentorship and networking, Visa Foundation can help unlock the potential of women-led businesses to drive further innovation in the UK.”
CatalyseHer is supported by the social enterprise IMCO. See more on the programme here.
Visa also runs the ‘She’s Next’ programme, a global initiative to spotlight women in their efforts to fund, run, and grow their businesses.
Launched in January 2019, it has invested £2.5+m in grants and coaching for female business owners across Europe.
Fertility
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Fertility
AI could transform ovarian care through personalisation, study finds

AI could transform ovarian care by personalising cancer and fertility treatment, but more clinical validation is needed before routine use.
A systematic review and meta-analysis found AI models showed high diagnostic accuracy for ovarian cancer when combining data such as ultrasound scans and blood test results.
Across 81 studies, AI models correctly identified ovarian cancer in around nine out of 10 cases, with pooled rates of 89 to 94 per cent.
They were also highly accurate at ruling out ovarian cancer when it was not present, with specificity of 85 to 91 per cent.
The analysis also found that explainable AI tools could predict complete surgical cytoreduction in advanced ovarian cancer.
Complete surgical cytoreduction means removing all visible cancer during surgery, which can be an important goal in treatment planning.
The tools achieved a pooled AUC of 0.87. AUC is a measure of how well a model distinguishes between different outcomes, with higher scores showing stronger performance.
In reproductive medicine, AI algorithms helped physicians optimise ovarian stimulation protocols and predict follicular growth during IVF.
Ovarian stimulation is the use of hormones to encourage the ovaries to produce eggs, while follicles are the small sacs in the ovaries where eggs develop.
The review found AI could reliably model ovarian response in IVF with a pooled AUC of 0.81.
However, researchers said challenges remain in translating promising research findings into routine clinical practice.
They identified substantial variation across studies, driven by retrospective study designs, variable AI systems and a lack of standardised validation.
Only 22 per cent of analysed studies reported prospective, multicentre external validation, where models are tested forward in time across multiple healthcare settings.
The authors called for rigorous validation to help close the gap between research and routine clinical practice, alongside standardised methodological and reporting frameworks, smooth integration with clinical workflow and robust governance to support responsible and ethical AI use.
They concluded: “Artificial intelligence is a transformative force in the management of ovarian conditions.
“In gynaecologic oncology, AI enhances every phase of care, from early detection and accurate diagnosis to prognostic stratification and surgical planning.”
In reproductive medicine, AI personalises ovarian stimulation and refines the diagnosis of heterogenous endocrine disorders such as PCOS.
PCOS, or polycystic ovary syndrome, is a hormonal condition that can affect periods, skin, weight and fertility.
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