A urine and vaginal fluid test could help rule out womb cancer in women with postmenopausal bleeding, potentially reducing invasive checks.

In a study of 1,864 women with postmenopausal bleeding, the test identified 80 of 99 womb cancers and correctly gave a negative result for 93 per cent of women without the disease.

Researchers said the test was not accurate enough to rule out cancer on its own, but could help fast-track women at the highest risk for urgent investigation while reducing immediate invasive testing for those at lower risk.

Professor Emma Davidson, of the University of Manchester and Manchester University NHS Foundation Trust, said: “Bleeding after the menopause is understandably alarming for women because it can be a sign of cancer, but the vast majority of those investigated will not have the disease.

“At the moment, many women face invasive, uncomfortable tests that can be stressful as well as costly for health services before cancer can be ruled out.

“Our study shows that a simple test using urine and vaginal samples could help identify the women most likely to have cancer while safely reassuring many others much earlier in the diagnostic pathway.

“If further studies confirm these findings in routine practice, this approach has the potential to transform care for thousands of women every year.”

Developed at the University of Manchester and Manchester University NHS Foundation Trust, the test examines cells collected from urine and vaginal fluid samples.

Postmenopausal bleeding currently triggers an urgent cancer referral, although only about 5 to 10 per cent of women who report it have an underlying cancer.

Current investigations include an internal ultrasound scan, a biopsy of the womb lining and hysteroscopy, in which a camera is passed into the womb.

Women taking part in the study attended seven hospitals in north-west England and provided urine and vaginal fluid samples before undergoing their routine tests.

Five per cent of participants, 99 of 1,864 women, were diagnosed with womb cancer through biopsy or surgery.

Cytologists assessed the samples without knowing the women’s diagnoses.

Researchers said the test could be used to triage women suspected of having womb cancer, with lower-risk patients monitored through repeat sampling or given a full assessment if symptoms continued.

They cautioned that the samples were assessed by highly trained specialist cytologists, so it is not yet known whether the same accuracy could be achieved in other settings.

The test has also not been studied in women without symptoms.

Mira has launched an at-home cortisol tracking kit that allows women to monitor stress-related patterns over time using urine samples.

The Cortisol Pattern Kit measures urinary free cortisol directly from a urine sample and displays results through the company’s existing monitor and app.

The system is intended for repeated testing rather than a single measurement, allowing users to compare results with a personal baseline built from their own data.

Mira said consistent daily testing can establish a baseline in about a week. It recommends testing in the morning and evening to see how cortisol patterns change throughout the day.

Cortisol is involved in the body’s stress response but is not a direct or complete measure of stress. The company said the kit is intended for general wellness use and is not designed to diagnose stress, an endocrine condition or any other medical condition.

The Cortisol Pattern Wands use the same lateral-flow testing format as Mira’s existing hormone tests. Users dip a wand into a urine sample, insert it into the Mira Monitor and view the result in the app within minutes.

The company said cortisol data can be viewed alongside cycle, hormone and daily routine information.

Sylvia Kang, founder and chief executive of Mira, said: “The Cortisol Pattern™ Kit gives women a new way to understand their stress patterns over time, in the context of their own biology.”

She added: “We built Mira to give women answers they had spent far too long searching for, beginning with the TTC journey and expanding into hormone and cycle tracking across life stages, including perimenopause. Now, we’re taking Mira beyond reproductive health and into everyday wellness, bringing personalised stress-pattern tracking directly into women’s homes.”

Mira said more than 350,000 women already use its platform to track their cycle and hormones.

Waitlist sign-ups for the Cortisol Pattern Kit and Cortisol Pattern Wands opened on 22 September, with wand refills available to pre-order in 20- and 30-count options.

Diadia Health, winner of the Femtech World AI Innovation Award, is working to revolutionise women’s health through AI-backed at-home testing.

By analysing genetics, biomarkers and medical literature, Diadia addresses women’s health issues, taking a functional medicine approach to healthcare.

With the goal of making personal healthcare accessible and affordable, Diadia Health uses technology to automate tasks for clinicians and doctors to save time and improve decision making, and provides comprehensive health reports.

Diadia app was founded by machine learning and AI scientist and CEO Elena Ikonomovska, and CTO Andrii Yasinetsky.

Ikonomovska speaks to Femtech World about the inspiration behind the app, how the app helps to find health issues traditional care misses and the future of Diadia in the healthcare system.

What was the inspiration behind the app and what was at the forefront of your mind when developing Diadia?

My research was about learning from infinite data streams, so for a very long time, I’ve been obsessed with the idea of teaching machines how to learn, in a similar way to how humans learn, which is incremental.

Early on I got into the space of machine learning and built multiple products and systems at companies like Google, Reddit, Change.org.

I also built a number of companies which were all AI first companies, always with a social impact in mind as I was creating these products.

I started experiencing health issues during my last company, and I’ve always been someone that has tried to be as healthy as possible – eating healthy, working out – but I was still becoming pre-diabetic.

My health was getting worse and worse. I spoke to four different doctors, changed primary care doctors, providers, and no one actually had any idea what’s going on, and didn’t know how to help me.

Being a scientist, I decided I was going to try to solve it by myself with the help of AI.

At this time, AI was already at a place where it was quite powerful, allowing us to research and read all the medical knowledge and interpret data.

I realised that there’s so much knowledge we have that has not reached healthcare providers because it is specialised knowledge.

The way that the healthcare system is organised is that everything is solved within a specialty, isolated.

The problems are looked at in isolation when, in essence, they’re not isolated. They’re very connected. Everything in the body is influencing everything else.

Through that problem, I actually started learning about this, and I started applying concepts of systems biology, which is used in functional medicine, an area of medicine which is very niche.

They look at the body as a whole system when they’re solving problems.

This is how I discovered answers for my health, over time, I understood the real cause for my issues was thyroid problems.

This was contributing to my insulin resistance and a number of other deficiencies such as iron anemia, that were contributing to the whole problem that needed to be solved all together, so that I could stop myself from becoming diabetic.

At Diadia, you combine genetics, biomarkers, and medical literature to uncover what normal testing tends to miss.

What was the breakthrough that made you realize AI could solve a problem that traditional clinical tools have struggled with?

                     Elena Ikonomovska

AI is capable of connecting the existing knowledge we have with questions and problems.

Initially, we built a system that was capable of seeing the problem only from the five biomarkers without further testing, and that made me realise that this is powerful because it can make the connections between these data points and that represent the different systems in the body intelligently.

This means that it understands the relationships, understands biology, understands how things are like, you know, interacting with each other.

The only thing that we were worried about is that sometimes it might be wrong, as it really doesn’t do proper logical thinking but pattern matches and connects information that statistically is likely accurate together.

There is no protection from fabricating little details that are wrong.

So what we call hallucinations are happening more and more, these are mistakes that AI does that are not obvious.

You can’t catch them by the eye, especially if you’re not an expert. You wouldn’t understand that this is not true.

What we did was we forced logical thinking, we forced logical connections between the evidence that the AI is capable of finding, so that we make sure that the conclusion at the end from the data that is being given is making sense.

It’s logical, and there is research and there is data supporting that connection. That’s something that chatbots don’t do.

I think that’s the reason that makes me sleep well at night because we know that this way we can connect not only genetics – we actually work with gut tests and metabolites, toxins analysis, infections, and all sorts of different tests and biomarker labs.

It enables a multi-specialist view on the problem when you’re analysing the data of the patient in the context.

There may be concern that AI might replace clinical judgment, but you have taken a different approach – what have you learned from working with clinicians about where AI creates the most value?

I don’t believe AI will replace clinical judgment, not yet.

For AI to be able to replace clinical judgment, it needs to be trained over highly dimensional data coming from specialty labs that represent the full body all at once, and such data does not exist.

There is no data of that sort, and also there is no decision making clinical frameworks or choices around how treatments should be ordered and sequenced out that is available to the AI to learn from.

This is knowledge that only clinicians have.

In fact, like the best clinicians have been creating such knowledge and frameworks for decades, practicing in this cutting-edge field of medicine, honing their skills and learning from experience, and embedding the latest research.

That’s the kind of knowledge that is needed to guide these systems to make better decisions over time.

That is also something we are working with clinicians on, because we know that AI cannot on its own come up with the best actual answers, and what we want is the best possible, and the most accurate right analysis, so that we’ll be able to safely deploy such technology to millions of people.

We are working towards that world, and clinicians are a huge part of it.

The more data we generate, the more knowledge we will create about our understanding of disease, human longevity and health span will create more human judgment to continue guiding the tool to uncover more and create more data.

This will be to feed back that data into clinical decision-making processes, because the space is so unexplored, it’s like we’re just entering right now.

At the end of the day, it has to be a human being accountable for another human, and also a human that is there to explain and help the other person incorporate all the things that they need to be doing for their health.

How does Diadia bridge the knowledge gap between patients and clinicians?

The AI is analysing data and prepares very comprehensive reports with clinical priorities and protocols. It explains why certain choices have been made, what it is addressing, and more.

For the patients, there is also really great research to learn more and read more. We give them all the medical research and then it gives them something to hold on to until next time they see their doctor.

Over time we are likely going to have features like chatting functionalities so that the AI will be able to answer certain questions based on the knowledge or the clinical guidelines from a specific clinic.

It is saving hours of analysis time that most doctors don’t have time to really look at. Once you start entering this complex data, it can be hours of analysis where you need to look at 300 biomarkers, or even 1000 in some cases. That’s a long time that a lot of doctors don’t really have.

The AI is in essence cutting that off and giving them a fairly comprehensive insight report that they can quickly understand, look into things, and adjust if needed, and then hand it over to the patient.

As AI and other elements of healthcare such as precision and personalised medicine continue to evolve, where do you see Diadia heading in the next five years, and in women’s health more broadly?

Right now Diadia is being used by clinics who are practicing functional medicine. What we’re capable of, is empowering clinicians to see more patients while maintaining the same high quality standards, as well as being able to grow their practices and train staff.

I hope that as this technology becomes better, we’re going to be able to then bring it into more accessible clinics like direct primary care, and eventually integrated into the healthcare system.

My big dream is that this technology will be covered by insurance. It will be helping millions of doctors in the U.S.

For now we’re in the U.S. market in order to provide this kind of quality care and ultimately offer the service to more women and men as well this personalised precision medicine care that I believe should be the standard of care for everyone.

Finally, what does it mean to win a Femtech World Award?

It’s really an amazing recognition.

As a woman, I put a lot of heart into this. My whole mission is to build a world where women and men, of course, will have the right kind of care that we need, and we will have better data.

I hope that some of the bias and unfairness around women’s health will be fixed.

And so, being recognised that we’ve made a contribution in this direction means a lot to me.

It really helps me do this work and feel more inspired to continue forward.