Medical Design Briefs - February 2022 - 28

The important thing in all these use
cases is that the medical devices must
have the computing power to record
and evaluate information auton -
omously. This calls for AI-based edge
systems that work in operating rooms,
emergency admissions departments,
or intensive care units independently
of any cloud connection. Through
this, data evaluation in the cloud is
avoided and, as a result, latency is
reduced, and devices remain operational
regardless of the availability status
of the network.
The Congatec starter kit for AI vision integrates the individual components, with a Basler dart camera
acting as the camera, a SMARC 2.1 carrier board with 2x MIPI CSI as the communication interface,
and a SMARC 2.1 module as the processor. (Credit: Congatec)
AI in Radiology and at the Bedside
With AI in radiology, it is possible to analyze information
from x-ray and ultrasound machines and use it when obtaining
findings. For example, a doctor examining a lung with a CT
scan can look at the entire thorax and thus also be able to
detect calcification of the coronary arteries. This additional
information is helpful when it comes to comprehensively planning
and initiating the treatment for a patient. An AI system
compares the patient values with reference values stored in the
software. Any deviations or abnormalities are marked in an
image so that the doctor can examine the corresponding areas
more closely.
At the bedside, a processor with an NPU is a digital partner
that intensifies the patient care. AI in electrically operated
beds can learn which positions a patient prefers and what kind
of support the patient needs when sitting or getting up. AI
applications also monitor vital signs such as blood pressure,
temperature, or ECG data and control infusion pumps. This
enables controlled delivery of fluids such as blood, medications,
and nutrients to patients via the circulatory system. In
addition, AI solutions can support medical staff in communicating
with patients - especially when the patient is no longer
able to speak clearly due to a medical condition.
Simplified Human-Machine Interactions
Control of medical human-machine interfaces (medical
HMIs) via touchscreen, voice, or augmented reality is also
included here. This not only simplifies operation of patient
monitoring systems during anesthesia or at the bedside but
also, thanks to the integrated AI processor, relieves physicians
and care staff through activation of the corresponding
early warning systems. In addition, AI solutions are increasingly
being used in so-called hospital admission machines
for patient admissions to hospitals. They support staff in
emergency admissions by providing recommendations for
the treatment order based on vital signs data such as blood
sugar or lung function in real time to avoid dangerous
bottle necks. Using AI to create a digital twin of a patient to
test therapies virtually in advance is even being envisaged
for the future. This would allow doctors to assess possible
effects of medications or therapies and minimize possible
risks.
28
Cov
Compact Starter Kit for Patient
Care
To accelerate the use of the i.MX 8M
Plus processor in all these possible AI
applications, a starter kit enables de -
velopers to implement edge biomedical engineering applications
quickly and safely. Given the diversity of possible medical
applications, it goes without saying that the starter kit has to
allow embedded applications to be designed to fit perfectly. The
kit is supplied with a credit card-sized SMARC 2.1 computer-onmodule
with an i.MX 8M Plus processor.
The integrated NPU accelerates the local execution of AI
inference at the edge completely without a continuous cloud
connection. For vision-based tasks, the module integrates
hardware-accelerated video encoding and decoding in the
particularly data-efficient H.265 video compression standard.
Through this, even high-resolution video streams from
the two integrated MIPI CSI interfaces can be transmitted
directly over the network to other e-health solutions or to
the hospital PACS (picture archiving and communication
system).
Tailored Adaptation Via eIQ
On the software side, an " eIQ Machine Learning " platform
- " eIQ " stands for edge intelligence - provides developers
with access to a development environment for their AI-based
systems. The platform combines different libraries and development
tools and is tailored to microprocessors and micro -
controllers from NXP.
Software-based inference engines that apply logical rules to
existing data and findings in order to deduce new facts are
included. The eIQ platform supports inference engines and
libraries such as Arm Neural Network (NN) and the opensource-based
TensorFlow Lite.
The Seeing AI Eye
The application-specific design is ultimately implemented
via the 3.5-in. carrier board. It serves as the central interface
for data communication and links all the required peripherals
to the module. For example, it features two MIPI CSI-2.0 interfaces
for easy connection of cameras without the need for additional
converter modules. The starter kit comes with the Basler
dart BCON MIPI camera, but any OEM camera can be connected
as long as it is compliant with MIPI CSI and is supported
by the Congatec kit.
On the software side, the Basler pylon Camera Software
Suite even supplies a uniform SDK that is also suitable for interwww.medicaldesignbriefs.com
ToC
Medical
Design Briefs, February 2022

http://www.medicaldesignbriefs.com

Medical Design Briefs - February 2022

Table of Contents for the Digital Edition of Medical Design Briefs - February 2022