Medical Design Briefs - April 2023 - 27

ment, even providing a continuous recording of cardiac activities
before, during and after exercise, " says Xiaoxiang Gao, a
postdoctoral researcher in the Xu group at UC San Diego.
n The Importance of Cardiac Imaging
Cardiac diseases are the leading cause of death among the
elderly and are also becoming more prevalent among the
young due to lifestyle factors. The signs of cardiac diseases are
transient and unpredictable, making them hard to spot. This
has upped demand for more advanced, inclusive, noninvasive,
and cost-effective monitoring technologies such as long-term
cardiac imaging, which this wearable device facilitates.
Cardiac imaging is one of the most powerful tools for screening
and diagnosing cardiac issues before they become problems. " The
heart undergoes all kinds of different pathologies, " says Hongjie
Hu, a postdoctoral researcher in the Xu lab at UC San Diego. " Cardiac
imaging will disclose the true story underneath. Whether it be
that a strong but normal contraction of heart chambers leads to
the fluctuation of volumes, or that a cardiac morphological problem
has occurred as an emergency, real-time image monitoring on
the heart tells the whole picture in vivid detail and visual effect. "
n How It Works
The new system gathers information through a wearable
patch as soft as human skin, designed for optimal adherence.
The patch measures 1.9 (L) × 2.2 (W) × 0.09 (T), about the
size of a postage stamp. It sends and receives the ultrasound
waves which are used to generate a constant stream of images
of the structure of the heart in real time. This ultrasound
patch is soft and stretchable, and it adheres well to human
skin, even during exercise.
The system can examine the left ventricle of the heart in separate
bi-plane views using ultrasound, generating more clinically
useful images than were previously available. As a use case,
the team demonstrated imaging of the heart during exercise,
which is not possible with the rigid, cumbersome equipment
used in clinical settings.
The performance of the heart is characterized by three factors:
stroke volume (the volume of blood the heart pumps out each
beat), ejection fraction (the percentage of blood pumped out of
the left ventricle of the heart every beat) and cardiac output (the
volume of blood the heart pumps out every minute).
Xu's team developed an algorithm to facilitate continuous,
AI-assisted automatic processing.
" A deep learning model automatically segments the shape of
the left ventricle from the continuous image recording, extracting
its volume frame-by-frame and yielding waveforms to measure
stroke volume, cardiac output and ejection fraction, " says Mohan
Li, a master's student in the Xu group at UC San Diego.
" Specifically, the AI component involves a deep learning
model for image segmentation, an algorithm for heart volume
calculation, and a data imputation algorithm, " says Ruixiang
Qi, a master's student in the Xu group at UC San Diego. " We
use this machine learning model to calculate the heart volume
based on the shape and area of the left ventricle segmentation.
The imaging-segmentation deep learning model is the first to
be functionalized in wearable ultrasound devices. It enables
the device to provide accurate and continuous waveforms of
key cardiac indices in different physical states, including static
and after exercise, which has never been achieved before. "
Medical Design Briefs, April 2023
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Medical Design Briefs - April 2023

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