How AI helps women track hormonal health, with Marina Pavlovic Rivas

MARINA PAVLOVIC RIVAS: So I was experiencing different hormonal symptoms … there’s different symptoms that can happen throughout the month, throughout the year, from year to year … when it comes to hormonal contraception, getting off hormonal contraception throughout the menstrual cycle, and speaking with hundreds of women around me realize that it was very common.

RANA EL KALIOUBY: Marina Pavlovic Rivas wanted answers to her questions around hormonal health. And to do that she needed data. As a data scientist, Marina realized how little she actually knew about her own body. But the problem didn’t stop with her – there are huge gaps in research when it comes to women’s hormonal health.

RIVAS: Very often when we think of hormones, we think of fertility, and for sure that’s part of the picture. But it goes much beyond that. Hormones play a role in all body function, but until now didn’t have the information to be able to see how those fluctuations ultimately impact health.

EL KALIOUBY: Marina wants to change the way we think about hormonal health. She’s co-founder of Eli Health – a company making the first at-home monitor that tracks your hormonal levels using saliva.

Women’s health — and in particular hormonal health – is something near and dear to my heart. It affects so many of us — yet it’s a conversation we aren’t having. Today, we’re changing that. Marina and I are talking about the roots of the women’s health gap, how novel sensors and AI can democratize access to health, and why we need this longitudinal data.

I’m Rana el Kaliouby and this is Pioneers of AI – a podcast taking you behind-the-scenes of the AI revolution.

[THEME MUSIC]

Hi, Marina.

RIVAS: Hi Rana.

EL KALIOUBY: Welcome to Pioneers of AI. It’s so great to have you here.

RIVAS: Thanks for having me.

Copy LinkWhy women's hormonal health has been overlooked for so long

EL KALIOUBY: So today we’re gonna talk about women’s hormonal health, which is of course one of the most powerful, yet most misunderstood aspects of women’s physical, emotional, and mental wellbeing. And it’s something that impacts our fertility to our mood, our longevity.

It’s so underfunded both in research and in innovation, which is why I’m so excited to have you on the show and also learn about your company, Eli Health.

But before we do that, I would love to hear more about your story, ’cause I think you have quite an untraditional route into tech.

RIVAS: Yes. So my background is in data science. Originally, prior to that, I was in communication. That combination may seem unusual, but for me it was the same passion of putting information in the hands of people and making it available at scale. I had the first company in the field of AI. After completing my studies and after the acquisition of this company, I started a, from a personal need. Of course, with a background in data science, I value having data when it comes to making important decisions, but I realized that when it came to my health and hormonal symptoms, this data was missing. So this is what ultimately led to the creation of the company with my co-founder and making this tool accessible for the first time.

EL KALIOUBY: Yeah. Women’s health is a topic that I’m personally very passionate about and I’m committed to highlighting amazing founders who are building in this space. And we actually had Alicia Sean Rodriguez, the founder — you of course know her — the founder of Bloomer Tech. And she talked about her smart bras for cardiovascular health for women.

And we spend a lot of time talking about how come women’s health is so underfunded and so misunderstood? So why do you think this is the case?

RIVAS: Yes, there’s different reasons behind that. There’s more systemic reasons, of course, when it comes to different disparities when it comes to gender. Until not so long ago, women were not included in clinical trials. But there’s a piece of that that is also around historically the perceived complexity of women, that it was considered too complex and expensive to include women in clinical trials.

Given those fluctuations and hard to isolate variables. So that’s a piece of it. But more broadly, when it comes, for example, to startups and funding in the space, the presence of women both on the founder side but also on the investor side plays a big role because when it comes to women’s health, having investors that understand firsthand what those challenges are makes a big difference in being able to see an opportunity, especially when it comes to creating a technology that never existed before. Having that firsthand challenge enables investors to see that future much more easily.

EL KALIOUBY: Yeah, I always say who writes the check determines who gets the check. Right? And if you’re pitching to an audience who don’t really get the pain point and they think it’s a fringe problem — but of course women make up like 50% of the world’s population. So it’s not really a fringe problem, but if they haven’t experienced it, they won’t really see the pain point.

RIVAS: And we see often as founders many of us in the space receive the comment that women’s health is a niche. When it comes to healthcare buying decisions, 80% of the spending is controlled by women. So it’s far from being a niche. So from a venture standpoint, that underrepresentation also represents an opportunity for investors out there that understand that gap.

Copy LinkWhy hormone snapshots miss the full picture of health

EL KALIOUBY: Yeah. Absolutely. Okay, so we kind of already talked about how, for women, our hormones influence every aspect of our wellbeing, right? Yet for most of us, and I’ll speak for myself here, you go through life without actually any understanding of your hormonal patterns and the effect it has on your health.

And at best, maybe you do a blood test once or twice a year. It gives you a snapshot of your hormonal health, but it’s not really actionable because of course our hormones fluctuate on even a daily basis. And so you’re on a mission to change that, so tell us your approach.

RIVAS: Exactly, so that was part of the insights that led to the creation of the company, similarly to how it didn’t make sense to measure your heart rate once a year and then make decisions all the other days of the year based on that information. It’s the same for hormones. Having that single snapshot can yes tell you information.

Sometimes it’s enough, a first data point that can lead to a diagnosis, for example. But when it comes to using that information to optimize your health and wellness in a way that you can see how this information can guide lifestyle decisions, that single data point per year then becomes insufficient.

Similarly to how measuring your sleep once a year — would it make sense to see how your actions on a day-to-day basis impact your sleep? Same for heart rate and other biomarkers. So we’re introducing the same for hormones. That possibility of having the data frequently over time in order to see how your different lifestyle decisions impact this data.

Copy LinkWhy cortisol and progesterone are powerful starting points

EL KALIOUBY: So you kind of honed in on measuring cortisol. I’m curious why, and maybe you can use this as an opportunity to talk about some of the other hormones that could be of interest as well.

RIVAS: Yes. So currently we’re measuring cortisol and progesterone. And we started with those two hormones because of the key roles they’re having for health. Cortisol plays a big role across all body function. So for example, if cortisol is unbalanced, it becomes very hard to balance other areas of health.

To give a couple examples, when it’s 20% higher than baseline, that can be enough for ovulation not to happen. And as a result, not being able to conceive or having different challenges when it comes to sleep, mood, and other areas of health. It can lead to metabolic imbalances that then lead to weight gain, and same for cognitive health, physical performance. So starting with that hormone plays a big role for those other areas. And often we refer to cortisol as the stress hormone, but more and more—

EL KALIOUBY: More than that, right?

RIVAS: —they call it the master hormone. Because of that role it has really orchestrating the different systems of the body. And for progesterone, it’s one of the key female hormones. So being able to have this information throughout the month, throughout the years, then enables women to have that visibility on this biomarker that plays a big role.

Copy LinkDesigning a hormone test simple enough for daily life

EL KALIOUBY: So let’s talk about the form factor. Why saliva, and why?

RIVAS: Yes, so we used saliva. And for us, when we started the company, we had two — more than two, but two — key guiding principles: to have a test that is as easy as brushing your teeth and that can be as affordable as a cup of coffee. And the reason behind that is to enable that product and that technology to be a key piece of a daily routine. We’ve explored different form factors going from patches to contact lenses. We went really broad into the different possibilities and landed on saliva due to its non-invasive format, and also the possibility to do that testing anytime, anywhere.

We have users that are doing it in their car on the way to work, at the gym. So when it comes to seeing how this data correlates with lifestyle, that possibility of having information instantly and wherever you are plays a big role in choosing that form factor.

EL KALIOUBY: In a minute, Marina walks us through how this test actually works. Plus we talk about how computer vision can be an integral part of personalized medicine. Stay with us.

[AD BREAK]

Copy LinkHow at home testing turns hormone data into action

EL KALIOUBY: So walk us through how this would work. So say I order an Eli Health kit and it arrives at home. What do I do?

RIVAS: Yes. So, can I show one.

EL KALIOUBY: Yeah, that’s great. At this point in our conversation, Marina took out one of the Eli tests. It’s about the size of a pregnancy test you’d pick up at the drugstore – it kind of looks like one, too.

RIVAS: So you receive it in a pouch like this one. This is the test and you put this extremity on your tongue for a few seconds. Pull on the strip. And the saliva starts flowing, and when it’s fully developed, you see lines appearing on the test and you take a picture with your phone. And that picture is being translated into the hormonal levels, but also what they mean for you in the context of your own baseline and your own goals as well.

EL KALIOUBY: And so then, on this app, the app’s gonna do the analysis — and we’re gonna dive into the algorithm behind that in a second. But then, how is the outcome represented? What does it say?

RIVAS: So on the app you see, similarly again to a continuous glucose monitor or a smart watch, you see your hormonal levels, but also compare it against benchmarks. So you can see whether it’s within range or outside of range against the population, but also how it fluctuates versus your own baseline because you could be within the normal range but still fluctuate from one day to another.

So we highlight those variabilities and also provide different metrics like scores and recommendations around what you can do based on this pattern. So then you can create that feedback loop to see if the actions that you implement are actually affecting your cortisol or progesterone curve.

EL KALIOUBY: Okay. I have a whole bunch of questions for you. I’m a fanatic — I wear my Whoop like religiously because I love the data. It’s really insightful and actionable, so can you kind of share some of the recommendations that Eli could give based on the tests.

RIVAS: Yes. So when it comes to the recommendations, we focus on lifestyle, and those lifestyle categories are the classical ones that we’ve all heard about when it comes to exercise, sleep, food, other actions like light exposure, sleep consistency. However, the devil is in the detail.

For example, when it comes to exercise, it’s not simply about doing more exercise. Let’s say if you have a cortisol pattern that is too high in the evening, then it would not be recommended to do high intensity exercise later in the day. Versus if your cortisol is too low in the morning, then it can be recommended to do a higher intensity exercise early in the day. That’s just two examples of how in the same category, the type of recommendation when it comes to exercise intensity, timing, and recovery changes based on the patterns that are being observed.

Copy LinkHow often to test and who benefits most right now

EL KALIOUBY: You mentioned a continuous glucose monitor. And of course, the hormonal tracker equivalent of it would sample your hormone levels multiple times, say a minute. How often should one take a saliva test so that you’re getting an accurate enough picture?

RIVAS: We recommend at least four times throughout the month, and that’s really a minimum to see those fluctuations over time. So for cortisol, we’re interested in seeing the levels in the morning and in the evening. So one test in the morning, once in the evening. And to do that at least today throughout the month.

And for progesterone, given that the fluctuation happens throughout the month, those tests are across the month instead of being within the same day. And that’s a baseline. But what we’re seeing is that depending on the lifestyle and depending on the goals, there’s people that want to test much more frequently than that and others for which four times is enough.

EL KALIOUBY: Yeah, that’s great. Also similar to the continuous glucose monitors, obviously it started first with diabetic patients and now it’s becoming more popular just for kind of the average consumer, right? Who is your target customer for this kind of test?

RIVAS: Yeah. So in the industry when it comes, for example, to glucose, it first started in clinical settings to then evolve towards consumers. And then there’s other technologies like smart watches that started with consumers and that are now evolving towards clinical applications. We are more following the second path, starting with consumers and eventually evolving towards clinical use cases, for the simple reason that people want to have access to this data and have access to the information directly in their hands. Similar, I would say, type of behaviors as people who love seeing their data on their smartwatch, but that also understand that this is not enough for the full picture.

EL KALIOUBY: Yeah. Do you focus on a particular segment within women, like women who are trying to get pregnant or perimenopausal or menopausal?

RIVAS: So we’re seeing a lot of interest around perimenopause and overall health. Because during that transition, which can last more than 10 years, there’s a lot of symptoms and a lot of changes that are happening for the first time. And we see a big demand in having access to the relevant information during that phase in order to be able to manage symptoms and act proactively around those different areas.

Copy LinkHow computer vision makes hormone tracking scalable

EL KALIOUBY: Yeah. Okay, so we’re an AI show, so I wanna now take us behind the scenes and talk about the R&D that was involved in building the product and the role AI plays in all of this. I’ve already heard one area where I believe there’s gonna be an AI role, which is the computer vision algorithm that takes that strip and translates it to a score. So tell us more.

RIVAS: So from an R&D standpoint, this was a very multidisciplinary effort. Of course the chemistry itself was a big portion of that, the microfluidic aspect, the hardware. But when it comes to AI, as you mentioned, the computer vision detection was a big portion of this. Initially we used to have a dedicated reader into which we had to insert the test to take the reading.

So this had, of course, a couple of downsides including the price for the user and reducing ultimately the accessibility. So replacing that reader with the smartphone camera was a big step in the direction towards making this product accessible at scale.

And that’s something we couldn’t have done without computer vision algorithms. So that’s really a key role when it comes to AI for our system. And then of course the data interpretation, because those computer vision algorithms enable us to get the result, which is great. But then the next step in that process is to provide the interpretation of that result and also the different insights and recommendations that can be a next step to that result.

EL KALIOUBY: So I’m a computer vision scientist by background. And so I can see some of the challenges that may come up. For example, if I’m doing this test and it’s low lighting, right, that’s gonna affect the image the camera’s gonna take and may affect the results. So how do you guard against some of these challenges like lighting conditions, angle conditions, blurring photos?

RIVAS: Well, as you mentioned, those were the key elements that made this a challenging thing to solve. We were unsure when we started that development if we would even get to a similar performance as the reader. And today we have the same performance, due to the different data sets that we’ve collected in order to train the algorithms in a robust way. So that came, for example, using different cameras, using different lighting conditions, different angles across the different concentrations that can be found in saliva. So that’s one thing from the training standpoint, but then from the user interface standpoint, there’s also different guardrails that enable us to guide people, for example, in how the picture of the test is being taken. If it’s not aligned properly, there’s a message that appears on the interface to guide users into placing it the right way. Same for lighting. If it’s too dark, there’s information that guides users into what those minimal settings should be to take a picture.

EL KALIOUBY: Yeah. If any of our listeners do the mobile check function in your bank, in your mobile bank app, I think it’s very similar where you’re trying to take a picture of a check, but then it’ll guide you to position it right and ensure that the lighting is correct.

What’s next on the roadmap? Like, are there any other hormones that you’re particularly interested in having tested or included in the product?

RIVAS: Yes, there’s many other hormones that we plan to add in the pipeline, including DHEA and testosterone. Testosterone, which often when we think of that hormone we think of men. And of course it’s a key hormone for them. But it’s also playing a big role for women’s health, which is something that is not always known.

So that’s one we’re particularly excited about for the impact it can have for women’s health, but also eventually for men. And as we saw with our early users — you asked earlier about one of the surprises that we got with that early usage — seeing that men also have a big interest around testing their hormones was something we were excited to see.

EL KALIOUBY: That is so fascinating too. Right? Because why not?

RIVAS: Exactly.

EL KALIOUBY: We’re going to take a short break. When we come back – the health span revolution and why we need better data sets.

[AD BREAK]

Copy LinkWhy longitudinal data is key to the future of preventive health

EL KALIOUBY: So one of the reasons I’m super excited to have this conversation is I really do believe that we are at the cusp of a health span revolution, and it’s powered by this trifecta of sensors in general becoming much more available, then this multimodal data explosion, right? And longitudinal data, which you’re collecting, and then combining that with both predictive and generative AI. So I am curious, what are you seeing from your point of view and what gets you excited about this space and what other innovations are you seeing?

RIVAS: Yeah, so some of the things we find extremely exciting is, as you mentioned, the combination of that increasing interest around preventative health and longevity, but also combined with increased access to the data. And that was one of the reasons why we started this company, because our hypothesis was that to be truly personalized and truly preventative, you need to have a frequency of data.

That matches the frequency of life. And without this granularity in terms of information, our belief is that the algorithms can only be as good as the data they’re being fed with. So if that data is missing, then there’s some major limitation when it comes to that vision of having health in real time throughout our life.

So some of the things we’re extremely excited about is seeing more companies creating this bridge between the biological and the digital, especially in an era where models are more accessible than they’ve ever been. AI is now being accessible to the mainstream, which is amazing. But the data is still missing around some areas of health.

So we’re seeing — as counterintuitive as it may sound — that the hardware components, so really physical products that make that bridge, will be a key driver in that health revolution.

EL KALIOUBY: Yeah. Now you’re also building, I believe, history’s first large scale hormonal data set of this kind. Two questions there. How are you ensuring that this data’s protected? It’s very personal data. And two, what gets you excited about it? What are some of the questions that you would love to dive into once you’ve got that data at scale?

RIVAS: Yes. Well first when it comes to privacy, we put the control in the hands of users where they have the possibility to not share some data and not log the data they don’t want to log. But also ultimately in the background, making sure that data is structured properly, which means in an anonymized way.

So that when we do those analyses, it’s never linked to a specific user. From a data set standpoint, what we’re really excited about is how it will help uncover many of the gaps that still exist today when it comes to hormones. So seeing, for example, how different interventions can be powerful to change how someone feels on a daily basis.

But beyond that, one of the areas that we’re extremely excited about is this ability, using this data set around hormones, to prevent different conditions. There’s already some early data that shows, for example, based on the patterns observed around certain hormones, that it can predict an increased risk of bone-related conditions like osteoporosis, heart conditions, cognitive conditions. So being able ultimately to see how a certain pattern can potentially lead to those different outcomes and act preventatively to change that trajectory is something we’re extremely excited about.

EL KALIOUBY: Yeah. Super exciting. All right, last question. And it’s a question I ask all my guests on the show, and it’s one I think a lot about. So as AI becomes more powerful, smarter, more creative, even more empathetic — which is what I spent the last 25 years building — what do you think makes us human in this age of AI?

RIVAS: That’s a great question. I think ultimately there’s something special around human creativity. For AI, it’s different than other tools that we’ve seen in the past. Sometimes there’s that analogy that we’ve been through similar transformations in the past, going from a typewriter to a computer to now AI. I do find that fundamentally it’s different — that AI is not just the brush, it’s also a piece of the painter.

But the intention behind the process stays human. And that ability also to imagine the future — maybe it won’t be the case in the future, but today I do think this is a big strength of humans.

EL KALIOUBY: I love that thought that humans still kind of have this unique ability to paint a vision of the world that doesn’t exist yet. Well, thank you Marina, for joining us on the show. This was great.

RIVAS: Thank you for having me.

EL KALIOUBY: One of my favorite topics to cover on this podcast is how the trifecta of sensors, data, and AI will unlock personalized and preventative medicine. Not only will this kind of personalized medicine lead to more access globally – it has the potential to predict problems before they spin out of control. Ultimately, this trifecta has the potential to increase our health span and longevity.

My conversation with Marina has underscored the importance of harnessing AI for healthcare … especially for women. Women’s health has historically been underfunded and understudied. When it comes to personalized medicine, there’s a real opportunity to change that narrative. It’s an application of AI I am very passionate about so if you are building in that space, please do reach out. Email us at pioneers of AI at wait what dot com.