Bernhard Kappe, CEO of Orthagonal, began his Virtual Engineering Week presentation, “Using AI & Machine Learning to Improve Medical Device Design,” quoting Marc Andreessen, founder of Netscape. “Software is eating the world, in all sectors,” Andreessen wrote in an article in the Wall Street Journal. “In the future every company will be a software company.”

Kappe said he thought that statement has aged well. He shared another Wall Street Journal story, this time a review of the Apple Airpod Pros, one year after their initial review. “This is not a review of new Airpod Pros,” he explained. “This is a new review of the same Airpod Pros one year later, and it’s a much better review.” The difference, he said, was because of better software. Apple kept collecting data and feedback over that year to improve it.

This is where AI and real world data comes in, Kappe said. “From usage and scale, from other data about users, their environment, the world that they live in, and what they do, AI can discover new patterns and correlations that humans can’t and turn those into algorithms much faster than humans can and continue to improve them while humans are sleeping.”

Those same patterns are also happening in medical devices, Kappe said. He gave an example of his client, Quidel, which makes point-of-care diagnostics. The company’s Project Sniffles includes a small diagnostic device that can read a standard Sofia fluorescent immunoassay cartridge, and then images of the cassette are captured on the Sniffles device and transmitted via Bluetooth to a mobile app. The result is then interpreted using a proprietary AI software model that is downloaded and part of that mobile application. “This has a number of advantages compared to the previous versions,” Kappe noted. “The first is affordability. The manufacturing costs are less than 20% of the cost for their current Sofia platform. Second is mobility and connectivity. Cellular connectivity and Cloud integration enables testing in new markets beyond the traditional point of care.”

The third advantage is rapid manufacturing, Kappe continued, because the devices are much simpler to produce. “And fourth, and just as important, is the ability of the algorithm and the device to continue improving. The AI algorithm can be improved over time through continuous offline learning.”

Kappe shared some things that his audience should consider as part of their journeys into using AI. “The first thing is to really think about where AI might add value to your medical device in general,” he stated, adding that companies should also think about where they can take existing algorithms and improve them and continue to optimize them. “Often you’re combining both of these things–new trends with algorithm optimization,” he said.

“Second, you need to understand how complex your problem is, both in terms of the domain and the inputs,” Kappe continued. “The more complex these things are, the more data you’re going to need to train and verify your models.”

He cited the example of Quidel’s Sniffles, which uses a standard sa1 camera with a fixed distance and fixed lighting issues. “Now contrast this with using a smartphone camera to take pictures of skin to identify skin cancer lesions,” Kappe said. “In that case, you don’t have a standard assay, you don’t have one camera with a fixed distance. You don’t have standard lighting conditions. You’re going to need a lot more data,” he stressed, adding that planning around that need and understanding that from the start will be helpful in terms of thinking about the feasibility and what will be needed to design for this.

The third thing he urged companies to think about is the availability of a reference standard for an AI model. He gave an example of glucose monitoring (CGM). One of the things needed to assess the performance of a CGM system is to generate a mean absolute relative difference (MARD) score. MARD is the average of the absolute error between all CGM values and matched reference values, which are gotten from blood tests. “Getting those blood tests has a cost, and it’s not part of everyday practice,” Kappe said. “So you need to pay for those when you update your algorithm,” he continued. “That makes it much, much more expensive to evaluate and validate the new algorithms. So one of the things you really need to take into consideration is, how much is it going to cost to get that reference standard? How easy is it to administer that because that’s really going to impact your ability to continue to improve your algorithms, and demonstrate that,” Kappe explained.

Another thing to consider is data availability and quality, Kappe said, including a company’s own data. “How are you making sure that it’s good data, that you can trace where it came from and isolate bad data? Make sure that training data is separate from verification data, etc,” he said. There may also be third-party data, which might not always be medical-grade, and Kappe urged his audience to think about how they would deal with potential issues with that, such as bias.

“Now, of course, it would not be a medical device if you didn’t have to consider patient risk,” Kappe said. Patient risk is going to affect how much testing needs to be done, how much risk analysis and risk mitigation needs to be done, and how much scrutiny an algorithm will undergo by regulatory bodies, he said.

The last thing Kappe mentioned was how and where algorithms will be deployed. Deploying them in the cloud is in many ways the easiest, he said, because you have a lot of processing power, can scale easily, and can define the specific GPU or FPGA [field programmable gate array] or access that you’re using, he said.

However, he said devices typically don’t have the same processing power. “If you’ve engineered your apps from modularity, it can be pretty easy to update the algorithms in your apps, but there’s a lot more work and translating and testing your algorithms. Different smartphones have different floating point precision in their GPUs, often much less than what you would be able to get in the cloud where you trained this, and so you might need to do a lot more tuning of your algorithms and testing those for specific smartphones,” he said.

Kappe wrapped up his presentation by leaving his audience with some best practices. “First, whether you using AI or not, you should design for data capture from the start,” he said.

The second is to design processes for continuous improvement. “So you need to think about how to design for efficient updates. There are a lot of best practices out there for automation and software as a medical device that you can leverage here,” he said.

“With future machines I describe the human role as being shepherds,” Kappe concluded. “You just have to nudge the flock of intelligent algorithms. Just basically push them in one direction or another and they will do the rest of the job. You put the right machine and the right space to do the right tasks.”

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Data science and artificial intelligence are no longer buzz words in the biomedical research community. Physicians and other caregivers are increasingly being encouraged by hospitals and health insurance companies to utilize low-resolution dense biometric data captured using wearable medical devices. However classical healthcare heavily relies on high accuracy sparse datasets, i.e. patients are expected to get a thorough medical checkup once in a while, as opposed to continuous monitoring of a handful of vital parameters. The most significant impact of data science will be in helping physicians extract clinically relevant information from such dense low-quality data sets.

Radiologists and cardiologists are increasingly relying on automated high dimensional image processing algorithms to detect the likelihood of coronary artery disease from non-contrast chest CT scans. Similarly, radiologists and pulmonologists are using similar artificial intelligence based technology to identify clinically relevant structural and functional parameters of the lungs from chest CT scans. Understanding the basics of artificial intelligence will empower physicians to go beyond using these tools as black-boxes and deliver maximum impact for care pathways.

In this article, we have listed five such reasons why physicians and caregivers should learn about emerging technology such as data science and artificial intelligence

1. Diagnose using large volumes of data generated from continuous monitoring

With the advent of wearable medical device companies such as CloudDX, Snap40 and QuasaR clinicians can now look at continuous daily biometric data collected over months. Both primary and advanced data science techniques can be used to derive medically relevant outcomes from these dense data. Basic descriptive statistical results like the average resting heart rate could give you a quick understanding of the overall cardiac health of the patient. More advanced indicators such as stress index or LF/HF ratio of RR distance could be used to predict chances of heart arrhythmias more accurately. Data science will allow physicians to analyze these data sets both at local (days or weeks) and global (months or years) timescales, using a combination of both early warning scores and visual inspection of the data.

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When Merdis Wells visited the diabetes clinic at the University Medical Center in New Orleans about a year ago, a nurse practitioner checked her eyes to look for signs of diabetic retinopathy, the most common cause of blindness.

At her next visit, in February of this year, artificial intelligence software made the call.

The clinic had just installed a system that’s designed to identify patients who need follow-up attention.

The Food and Drug Administration cleared the system — called IDx-DR — for use in 2018. The agency said it was the first time it had authorized the marketing of a device that makes a screening decision without a clinician having to get involved in the interpretation.

It’s a harbinger of things to come. Companies are rapidly developing software to supplement or even replace doctors for certain tasks. And the FDA, accustomed to approving drugs and clearing medical devices, is now figuring out how to make sure computer algorithms are safe and effective.

Wells was one of the first patients at the clinic in early February to be tested with the new device, which can be run by someone without medical training. The system produces a simple report that identifies whether there are signs that a patient’s vision is starting to erode.

Wells had no problem with the computer making the call. “I think that’s lovely!” she says.

“Do I still get to see the pictures?” Wells asks nurse practitioner Debra Brown. Yes, Brown replies.

“I like seeing me because I want to take care of me, so I want to know as much as possible about me,” Wells says.

The 60-year-old resident of nearby Algiers, La., leans into the camera, which has an eyepiece for each eye.

“It’s just going to be like a regular picture,” Brown explains. “But when we flash, the light will be a little bright.”

Once Wells is in position, Brown adjusts the camera.

“Don’t blink!” she says. “3-2-1-0!” The camera flashes and captures the image. Three more flashes and the exam is done.

She says still planning to examine the images and backstop the computer’s conclusion. That reassures Wells.

The test is quick and easy, which is by design. People with diabetes are supposed to get this screening test every year, but many don’t. Brown says the new system could allow the clinic to screen a lot more patients for diabetic retinopathy.

That’s the hope of the system’s inventor, Michael Abramoff, an ophthalmologist at the University of Iowa and company founder.

“The problem is many people with diabetes only go to an eye-care provider like me when they have symptoms,” he says. “And we need to find [retinopathy] before then. So that’s why early detection is really important.”

Abramoff spent years developing a computer algorithm that could scan retina images and automatically pick up early signs of diabetic retinopathy. And he wanted it to work in clinics, like the one in New Orleans, rather than in ophthalmologists’ offices.

Developing the computer algorithm wasn’t the hard part.

“It turns out the biggest hurdle, if you care about patient safety, is the FDA,” he says.

That hurdle is essential for public safety, but not an easy one for a brand-new technology — especially one that makes a medical call without an expert on hand.

Often medical software gets an easy road to market, compared with drugs. Software is handled through the generally less rigorous pathway for medical devices. For most devices, the evaluation involves a comparison with something already on the market.

But this technology for detecting diabetic retinopathy was unique, and a patient’s vision is potentially on the line.

When Abramoff approached the FDA, “of course they were uncomfortable at first,” he says, “and so we started working together on how can we prove that this can be safe.”

Abramoff needed to show that the technology was not just safe and effective but that it would work on a very diverse population since all sorts of people get diabetes. That ultimately meant testing the machine on 900 people at 10 different sites.

“We went into inner cities, we went into southern New Mexico to make sure we captured all those people that needed to be represented,” he says.

All the sites were primary care clinics, because the company wanted to demonstrate that the technology would well without having an ophthalmologist on hand.

That extensive test satisfied the FDA that the test would be broadly useable, and reasonably accurate. IDx-DR surpassed the FDA’s requirement. Test results that indicated eye disease needed to be correct at least 85 percent of the time, while those finding no significant eye damage needed to be correct at least 82.5 percent of the time.

“It’s better than me, and I’m a very experienced retinal specialist,” Abramoff says.

The FDA helped guide the company’s software through its regulatory process, which is evolving to accommodate inventions flowing out of artificial intelligence labs.

Bakul Patel, associate director for digital health at the FDA, says that in general, the FDA expects more evidence and assurances for technologies that have a greater potential to cause harm if they fail.

Some software is completely exempt from the FDA process. A simple tweak in a routine piece of software may not require any FDA review at all. The rules get tighter for a change that could substantially alter the performance of an artificial intelligence algorithm.

The agency has years of experience approving software that is part of medical devices, but new algorithms are creating new challenges.

For one thing, the agency needs to be wary of approving an algorithm that’s based on a particular set of patients, if it’s not clear that it will be effective in different groups. An algorithm to identify skin cancer may be developed primarily on white patients and may not work on patients with darker skin.

And many algorithms, once on the market, will continue to gather data that can be used to improve their performance. Some programs outside of health science continually update themselves to accomplish that. That raises questions about how and when updated software needs another round of review.

“We realize that we have to re-imagine how we look at these things, and allow for the changes that go on, especially in this space,” Patel says.

To do that, the FDA is testing out a whole new approach to clearing algorithms. The agency is experimenting with a system called precertification that puts more emphasis on examining the process that companies use to develop their products, and less emphasis on examining each new tweak. Continued monitoring is another element of this strategy.

“We’re going to take this concept and take it on a test run,” Patel says.

Because many algorithms will likely be in a state of continual evolution, “it’s really important when a system is deployed in the real world that we monitor those systems to make sure that they’re performing the way we expect,” says Christina Silcox, a researcher at the Duke-Margolis Center for Health Policy.

She’s enthusiastic about the prospects of AI in medicine, while alert to some of the challenges the FDA will face.

“Right now we might see an update to a medical deviceevery 18 months,” she says. “In software you might expect to see one every two weeks or every month.”

Seemingly minor software glitches can occasionally have serious unintended consequences. One of the worst cases involved a radiation therapy machine that, in the 1980s, gave huge overdoses of radiation to some patients because of a software bug.

Researchers looking at more recent incidents identified 627 software recalls by the FDA from 2011 through 2015. Those included 12 “high risk” devices such as ventilators and a defibrillator.

Patel certainly doesn’t want to see a high-profile failure, because that could set back a promising and rapidly growing industry.

One challenge that’s beyond the FDA’s scope is figuring out how to resolve conflicting conclusions from rival devices. Genetic tests that are used to guide cancer treatment, for example, already provide conflicting treatment recommendations, says Isaac Kohane, a pediatrician who heads the biomedical informatics department at Harvard Medical School. “Guess what,” he says, “The same thing is going to happen with these AI programs.”

“We’re going to have built-in disagreements and no doctor and no patient will know what is right,” he says.

Indeed, IDx isn’t the only company that interested in using an algorithm to identify early signs of diabetic retinopathy. Among its competitors is Verily, one of Google’s sister companies, which is currently deploying its technology in India. (Google is among NPR’s financial supporters).

“Actually I’m quite bullish in the long term,” Kohane says, as he looks out on the burgeoning field of AI. “In the short term, it’s a wild land grab.”

He says we need the equivalent of Consumer Reports in this area to help resolve these disagreements and identify superior technologies. He would also like reviews to examine not simply whether a technology performs as expected, but if it’s an improvement for patients. “What you really want is to get healthy,” he says.

The cost of the camera and set-up for the IDx-DR systems is around $20,000, a company spokesperson said in an email. There are options to rent or lease-to-own the camera that can reduce the upfront costs.

The list price for each exam is $34, the spokesperson said. But it varies depending on factors including patient volume.

A technically accurate piece of software doesn’t automatically lead to better health.

At the diabetes clinic in New Orleans, for example, the system replaced a service that also checked for another cause of blindness, glaucoma.

Nurse practitioner Brown visually scans Wells’ images for signs of glaucoma, but that wouldn’t happen when the work is handed off to someone who lacks her expertise. Instead, the diabetes clinic staff will refer patients to get another appointment for that test.

Wells also got something that future patients might not – a review of her retina images, so she could see for herself any suspected issues. That interaction with a health care professional was also an important moment to talk about her diet and what she can do to stay healthy.

Chevelle Parker, another nurse practitioner, points to some silvery lines inside the eye’s blood vessels.

“That happens when your sugar levels are high,” Parker explains. “It can also be an indication of diabetic retinopathy. So we’re going to do a referral and send you on for complete testing.”

The software did its intended job. While Wells seemed a bit upset by the news, at least she has found out about this concern early, while there’s still time to protect her vision.

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