We've gone from growing embryos in desiccators, basically it looks like a fancy pickle jar that you put in the incubator filled with gas, to now we're using time-lapse incubators that have cameras attached to them that have servers where the data now goes into the cloud and all of the characteristics of how the embryo are dividing are analysed by large data sets, algorithms, and AI is now applied to that. So, it's been a wonderful journey to see IVF come from a very small, you know, kind of mom and pop to where we are today.
What brought you to the World Egg and Sperm Bank at this stage?
Now we're applying artificial intelligence, you know, that's really kind of what started the basis, you know, renewed our relationship again was that.
How is AI affecting the future of fertility?
Well, you know, specifically for egg banks, in general, it was the follicular stimulation. In other words, you know, the patients, whether it's an egg donor or an IVF patient, they have to take shots and medications in order to get their eggs to grow. And when you apply this to a specific egg bank, what we don't really know, and it was a great unknown was like, you know, why do certain donors do better than others? What can we learn from that? And because when you look at an oocyte, an egg, an unfertilized egg before the sperm has entered it, the egg is very plain looking.
It's very, you know, it's very hard to discern the quality of the egg. And so, in other words, it's very hard to study what the effects of that donor are on the resulting eggs. And we know as an industry that often patients don't understand why certain donors do better than others.
And we in the industry, when we look into the mirror, we often can't answer those questions. So now, with the application of artificial intelligence, we're able now to really look in levels that we've never been able to before, to look at the effects of these external medications on the resulting egg quality. And then that information that is also used to see how well these eggs do when they freeze, how well they survive the thawing process.
And then also we can use this information, you know, to be more transparent to the patient in terms of telling them like, you know, this is what the probability is of you getting, you know, X number of blastocysts. In other words, that is the embryo that at the stage that we want to transfer it. So, working with AI has really been a blessing because what it means now is that instead of everything kind of existing within a black box, we can create, you know, very clear objectives because what AI does is when you put the egg into the incubator, what happens is there is a little camera on the bottom.
And what it does is it focusses through the egg and it takes, you know, millions of little slices of this egg and then it goes into the cloud. And so all of these, you know, pixelated images are then analysed in ways that the human eye simply can't. And then what happens is that that data that's collected from those resulting eggs is then compared to eggs that have made blastocysts or made babies and all that.
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So, you can now correlate the imagery that you've collected through this microscope with outcomes. And that's just something that we've never really been able to do before. And what we're going to do at the egg bank now is really apply AI in a routine basis so that we can create the highest quality of eggs, how to individualise the follicular stimulation process, you know, for donors and things like that.
And really what it does is it creates a lot more transparency across the whole area. Instead of telling a patient, we don't really know why you didn't do very well, we'll kind of be able to manage those expectations up front. And then internally, it allows us to, you know, to apply much more rigid quality control aspects and it objectifies it.
It can be used in a wide range of ways. It does create more transparency and objective outcomes.
What is it that people entering this part of their fertility journey know about egg and sperm donation that they do not know today or that they might misunderstand?
Unfortunately, menopause may have come and gone or it’s often people starting their own families. From the guy's perspective, there's, you know, we often have gentlemen who cannot produce sperm that are capable, so they must go outside.
And so, I kind of thought it was very willy-nilly, you know, like, you know, you just follow a certain set of protocols. But what I have learned through all this, particularly from the oocyte perspective, is that a lot of things have started to collide in the world. And that is that we have got the corporatization of IVF now has occurred.
And so, you are seeing a lot of what were small, successful clinics being put together into large networks. And these networks often, you know, have international arms to them. In other words, how a donor that's in Eastern Europe is screened is not necessarily the same as how a donor in your facility is screened. And so, and then couple that with the profit margins that the corporations have started to realise with this, it's kind of led us to where, you know, we don't have the standardisation that we really wish we could have. And there's a lot of factors other than, you know, creating the highest quality egg for the patients now that are kind of driving, you know, this area of medicine.
And so that's probably the biggest thing that we've seen is the thought that everybody across the world did this the same. Because if you think about it, you are taking eggs from a young lady and she needs to understand that, you know, she's got to be treated well in all of this. And she's got to be treated with respect.
What's more important? What's more individual than that? I mean, there's nothing more on an individual basis than that. And so, in my mind, I thought everybody kind of would follow these same rules. That often egg donors are, they're just not treated the same, you know, and I think that's probably been my biggest eye opening from my world travels over the last few years. AI will actually help us create that consistency and the standardisation that you're looking for and that we've always abided by. So, we are really honoured to have you guide us and join our team.
When a patient starts an IVF cycle, what we try to do is get those follicles to grow over about a 10-to-12-day period, because what happens is that the eggs, they need a certain amount of time to mature, but they also need a certain amount of time to assimilate all the necessary factors that they need. And so, by using AI, what that is really going to let us do is objectively look at like if we start the patient on a certain dosage of medication and how we handle those dosages throughout the treatment, what is the subsequent effect on egg quality on that? And then and so that's one issue. The other issue is certain donors, for example, in our industry, they make you know, they make virtually 100 percent of their embryos are utilised. And yet there's other donors that seem to fit the same characteristic, both, you know, from an age perspective, but all the diagnostic testing is done. And yet those donors do not do very well and we do not know why.
So, it's kind of the experiments that the patients experiment on the first go, whereas in our case, because we are going to have an objective scoring of the egg, we are going to know and we also get a prediction on what embryos or how many of these eggs are going to become usable blastocysts. Then then we know up front internally, do we like this donor? Yes or no. Do we want to use this donor? You know, if we if this is a donor that we really like a lot, how do we adjust the medication? And then as a result, we could also be very transparent with the patient saying, you know, we froze these eggs.
This is what their blast, you know, their probabilities are. And again, it just creates a level of managing expectations both internally and externally. The most important thing, because, you know, for those of us that work in IVF clinics, we dread the phone call to a patient saying, you know what, we do not really have anything right now or your eggs did not survive the thaw process.
Well, we certainly appreciate it as a as a as a parent of donor children, anything that we can do to advance the science to make it easier for people coming and trying this egg donation and sperm donation, which has high probabilities these days.
And guess what? We have a relationship with the donor or we know who the donor is. Then that bridge of accountability becomes even that much more important for us all, both how the donor was managed and taken care of the respect that was shown to her. But then also it puts a lot of pressure on us to because now we have personalised this that much more.
So being able to incorporate technology to pull all this together to kind of create the transparency, that's kind of the theme now of our discussions, really becomes that much more important.
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