Matthew Sappern: Where computers are so helpful with that, as you can imagine, is computers don’t get tired. They’re not getting coffee or arguing with someone. They look at the same series of data the same way every time. Once we figured out the ability to interpret these waves, we’re able to let the doctors or nurse know when there’s an issue at hand.
Even more critical is we can now take multi-factorial looks. We can be looking not just at the fetal heartrate. We can be looking at the mom’s vitals or labor progress. We can take all of those factors into account in a millisecond and look at it over a long stretch of labor.
There are certain things that computers are especially good at. Pattern recognition of multifactorial components is really one of the greatest things about AI solutions. That’s what we do. We provide a safety net that helps augment what clinicians are doing. Where there are certain areas of care that require a very objective view, we help reaffirm that object view.
Sramana Mitra: What is the level of penetration of your kind of technology in the hospital systems of America?
Matthew Sappern: It’s nascent. Healthcare is not a speedy adopter of technology. That vast majority of physicians and nurses feel very comfortable with the information that they gleaned while in school and studying the process behind it. It’s rare to find clinicians who are looking around anxiously for the latest innovations.
Since we’re dealing with humans, we all have to be remarkably careful. We are very careful in how we develop our software. It goes through an FDA clearance process. We are just now starting to look at what tools like AI and other machine learning technologies can really bring to healthcare. It couldn’t come a minute too soon because there are some pretty significant trends going on in healthcare that I think we’re only going to be able to manage with the help of technologies such as Perigen.
Sramana Mitra: Double-click down and talk about how you’re applying AI. What are the algorithms doing within your dataset?
Matthew Sappern: It’s purely about pattern recognition. We’ve developed the algorithms using TensorFlow that are developed in order to recognize patterns that shouldn’t be occurring. If we see something, it’s essentially like a needle in the haystack. The issue is the frequency of that outcome is not that great. You become very used to looking at heart rate patterns or rates of contraction that are okay. When a challenging one or a non-reassuring pattern comes across your screen, your eyes gloss over. That’s the real issue.
Whenever you’re trying to find these needles in the haystack, that’s a great opportunity for some sort of pattern recognition tool. That’s what we build with our AI applications. We have to take those algorithms and we then have to lock them down. We can’t be constantly evolving these algorithms. They have to go through an FDA clearance process. There is a fair amount of rigor and review that ultimately goes into that process.
I know where some people have mentioned to me that it’s not really AI unless it’s perpetually changing and learning all the time. I understand their perspective, but when you’re dealing with an FDA-cleared medical device, you can’t have that level of variability. What we try to create is a set of algorithms that recognizes patterns. I’m going to use the words of the National Institute of Health as they reviewed our solution. They found that we had substantial agreement with experts’ evaluations. In our mind, that’s the right fit for tools like ours – trying to put an expert evaluation at every bedside as opposed to consistently learning in real-time.