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 * AI tech revolutionizes medical device design

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AI TECH REVOLUTIONIZES MEDICAL DEVICE DESIGN

July 14, 2022 Sally Ward-Foxton

With over 350 FDA–approved medical devices using AI, the technology is enabling
portable MRI machines, foolproof ultrasounds, and more.

Latest developments in AI hardware and software are revolutionizing medical
device development, where the technology is now widely used to process, predict,
and visualize medical data in real time. AI is used in more than 350
FDA–approved medical devices and countless more that offer operational savings
in the healthcare environment.


Kimberly Powell (Source: Nvidia)

“The fact that we can now put artificial intelligence in instruments [means]
magic starts to happen,” Kimberly Powell, vice president of healthcare at
Nvidia, told EE Times.

For example, Caption Health has developed an ultrasound system which uses AI to
provide guidance and interpretation right on the ultrasound image for the
operator. The additional guidance means a broad range of healthcare workers can
perform ultrasounds, not just specialist sonographers. After selecting the type
of scan to be performed, the system walks the operator through getting a
high–quality picture, including showing the direction to move the probe with
arrows.



“You’re doing computer vision constantly on the probe data that’s coming in, so
you can direct them where to go,” Powell said. “Our anatomy is similar enough
that you can get them in the zone and then when it detects whatever they are
trying to measure, it can stop and automatically take the measurement.”

click for full size image

Caption Health’s guided ultrasound system directs the sonographer, who may not
be an expert, where to move the probe to take the best picture (Source: Caption
Health)

click for full size image

Caption Health’s guided ultrasound system is based on AI – specifically a
7.5–million–parameter convolutional neural networks (click to enlarge) (Source:
Caption Health)

It’s the first system of its type to gain FDA approval. Crucially, it’s a
physically small system that can be moved around a hospital as needed, or
combined with easing the requirement for a trained professional, can be more
easily used in the field or in parts of the world where fewer trained
sonographers are available.

“We have now the computing architecture that can go inside that device and do a
lot of AI right in that device, to provide that that user experience and that
fidelity of information insights that you can capture from the data,” Powell
said.

AI is also helping to miniaturize MRI and CT scanners. MRI machines today
typically require infrastructure that fills a whole room. Hyperfine has built an
AI–enabled MRI machine that is portable — it can be wheeled around the hospital
to the patient’s bedside or into the operating theater. The system, named Swoop,
has been FDA approved and is already in use serving patients in remote parts of
Canada and further afield.

click for full size image

The Hyperfine Swoop portable MRI scanner in action at a patient’s bedside
(Source: Hyperfine)

In this case, AI enables satisfactory results from the portable MRI scanner by
correcting for noisier images. This means lower field strength and lower–quality
sensors can be used, with AI making up the difference in image fidelity. Powell
compares this technology to smartphone filters which can fake smooth skin on
selfies — because we know what the end result should look like, this makes it
easier to correct for noise, she said.

Powell also said that AI is revolutionizing surgery, especially for modern
minimally invasive surgery techniques where the surgeon only has a camera view
into the body.

“You can add a lot of really powerful information on that camera view — don’t
cut this vessel, what is this anatomy over here — you can really help orient the
doctor,” she said. “And [surgeons] can now train in these simulation
environments to know exactly the procedure they’re going to do and the
trajectory they’re going to take.”

Patient Privacy

Is there a worry that noisy images augmented by AI will start seeing things that
aren’t really there?

Powell said that while false positives are possible, the new breed of AI–powered
devices goes through the same rigorous regulatory process as any other medical
devices, including clinical trials.

“From a quality assurance and regulatory perspective, they go through the same
due diligence, they have to go through the clinical trial phase, they have to
provide the evidence. The FDA and regulatory bodies have the same very strict
ruling of how you define whether this is performing the way it’s supposed to
perform.”

There are more than 350 FDA–approved medical devices that have functionality
based on AI. Powell pointed out that many more applications are not on the
regulatory pathway, including AI applications that aid operational efficiency,
of which several thousand are in use today.

“The amount of work that humans have to do from start to finish to acquire a
medical image is a lot, and the amount of potential error is a lot, and the
amount of time is a lot, so there’s a lot of [potential to improve] operational
efficiency there,” she said. “You’re still presenting the information to a
physician who is going to accept or decline the recommendation.”

As with existing computer vision technologies, wherever images of patients are
captured there are privacy concerns to be addressed. Performing AI in the device
itself in real time, without having to transmit images to the cloud for
processing, helps.

“The fact of the matter is we’ve been digitizing patient data for two decades;
it’s just [a question of] how is that data flowing, and who’s getting access to
it?” Powell said. “The appropriate regulation has to be set in place, and
business data agreements and all of that has to be very carefully looked after,
both for the purveyor of the data and anybody who’s receiving it. That’s just
fact.”

Powell points out that for AI–assisted robotic surgery, images taken within the
body have nothing to identify the patient. Clara Holoscan also includes
de–identification steps such as automatically deleting images that contain skin
and hair (which could potentially be used to identify the patient). These images
are useless to the robot surgeon anyway.

click for full size image

AI can help guide and advise surgeons during minimally invasive operations
(Source: Nvidia)

“It will take trust from patients, but I think in the end we’re all getting more
and more used to [being caught on camera],” Powell said, adding that even
doorbells today use cloud–based AI processing on video footage. “But for all the
companies I’ve worked with, de–identification is top of the list of AIs that
they develop first.”

Development Limitations

What are the limitations on using AI in medical devices today? Powell lists
three key areas. One is suitably powerful compute platforms that can handle
complex AI in real time without sending data to the cloud, which is why Nvidia
has developed Clara Holoscan.

Clara Holoscan is one of Nvidia’s three robotics platforms (the others are
Drive, for self–driving vehicles, and Isaac, for robots that function in the
human environment). It includes hardware (based on the Nvidia Jetson AGX Orin)
and software tailored to the development of medical devices.

“We call it a robotics platform because it’s really meant to create real–time
intelligent instruments,” Powell said, adding that while robots may perform
surgery unaided in the future, future X–ray machines or medical microscopes may
also be classed as robots if they have a robotic radiologist or scientist
inside, looking at the pictures for anomalies.

click for full size image

Nvidia’s Clara Holoscan platform can be used to design AI–powered medical
devices with any kind of sensor, from endoscopes to MRI machines (Source:
Nvidia)



Clara Holoscan enables the connection of any kind of medical sensor — be it an
ultrasound, endoscope, or CT machine — with powerful AI compute that can be done
in real time. Other medical–specific features include high speed I/O; AI
processing for medical physics, medical images, and medical data; and
acceleration for 3D graphics rendering.

“The nature of real time in this environment is that you’re literally helping
human in the loop become better at their job,” Powell said. “Clara Holoscan is
not only that… it’s also creating a Tesla moment for medical devices.”

Powell described how medical devices used to have a shelf life of perhaps 10
years. With Nvidia’s platform, new AI algorithms can be created and uploaded as
necessary to make the machines smarter. This can be done over the air. The
result is medical device makers shifting their business models towards
software–as–a–service.

“Now they have a computing platform that is not only very AI capable and can run
these real time applications, but can be remotely updated,” Powell said. “It’s
almost like bringing cloud capability to your medical device so that new
applications can be deployed, and these sensors can get better and better and
better, every few weeks that go by. That’s tremendously exciting for them from
an economical perspective.”

Training Data

Another thing holding back AI in medical devices is the limited amount of
training data available for training medical AIs, particularly those which look
for rare diseases or conditions. The solution here is more AI — specifically,
using AI to create synthetic training data for specific diseases. Nvidia and
King’s College London recently announced the use of Nvidia’s Cambridge–1
supercomputer to create a dataset of 100,000 synthetic images of brains that can
be used to build AI applications to accelerate understanding of dementia,
Parkinson’s, and other brain disease.

AI can create images of brains for specific segments of the population that may
be under–served in real datasets, such as women or young people, or people with
particular diseases. The same team is hoping to expand coverage to any part of
human anatomy in any mode of medical imaging (MRIs, PET scans, X–rays, etc).

The final limiting factor is the development of medical AI algorithms. To tackle
this, Nvidia has built a medical–specific AI framework called MONAI. MONAI
contains all the tools needed to label data, create synthetic data, train
models, validate models in the real world, then connect them to the Clara
Holoscan platform for deployment. This open–source platform is optimized for the
unique formats, resolutions, and metadata of medical imaging.

“It used to be that only the very rich and famous in their AI research labs
could do this stuff — we try to make it very available,” Powell said, adding
that when new medical papers are published, they are added into MONAI as quickly
as possible.

“We put it right into this open–source science framework so the world can
rapidly reproduce it and build upon it. This is why the pace of innovation has
gotten so quick — it’s because of this open science, open innovation world that
the AI community really instilled upon the world, which is good… I think AI
should be democratized in order for it to remain safe and productive.”

>> This article was originally published on our sister site, EE Times.



--------------------------------------------------------------------------------

Related Contents:

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 * AI brings better solutions to hearing aid wearers
 * Key trends in AI inference for 2022
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