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 3. Medical Imaging AI Advances Muscular Dystrophy Diagnosis


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MEDICAL IMAGING AI ADVANCES MUSCULAR DYSTROPHY DIAGNOSIS

Published Date February 17, 2023 Author Teresa Meek



Muscular dystrophy, an inherited disease with several variants that can appear
anywhere from early childhood to middle age, is exceptionally tricky to
diagnose. First, the patient’s genetic profile must be plotted out in great
detail. Then it is examined and compared with large sets of genomic data stored
at research centers and hospitals. Analysis is painstaking, requiring physicians
to do a great deal of manual work. The entire process can take 24 weeks to
deliver results.

A new method uses high-performance computing and AI inferencing to do much of
the heavy lifting, relieving doctors of tedious manual labor. By pointing them
in the right direction, it can shorten the time to diagnosis to 16 weeks or
shorter, depending on patients’ condition. That’s also good news for patients
and their families, who can start treatments sooner, and for researchers, who
can advance their knowledge of the disease.


COLLABORATION LEADS TO IOT HEALTHCARE SOLUTIONS

This new diagnosis method came about through the close collaboration of two
Taiwanese companies. Avalue Technology Inc., a provider of IoT computing
equipment, has deep experience with hospitals and labs. Biomdcare Corporation
specializes in medical software, imaging, and screening tools. Together, the
companies developed a muscular dystrophy screening kit that can analyze massive
genomic data quickly and deliver results that are 97% accurate.

To create the Genomics Analysis Platform – Muscular Dystrophy Screening Kit,
both companies had to overcome the vexing challenges that make working with
medical data so time-consuming and difficult. “We worked together to develop
hardware and software with the latest technology,” says Rus Lu, Senior Product
Manager at Avalue.

Avalue was tasked with finding a way to efficiently transport and process the
enormous sets of data to be analyzed.

“We knew the solution required a very high-performance CPU, so we applied the
latest Intel® Processors,” Lu says. The company also built an extra graphics
card slot into its server to accommodate the dense medical images and used a
10-gigabit Intel Ethernet chipset to avoid common bottlenecks slowing data
transmission.

The Biomdcare software had to identify the patient’s disease variant and
classify any correlations among the vast stores of genomic data it was able to
use for comparison. Working with data sets this large is beyond the processing
capability of most medical laboratories, and is usually confined to research
centers and universities.

Biomdcare used the Intel® OpenVINO™ Toolkit to develop an AI-assisted software
program that combs through all the data, filters out irrelevant results, and
homes in on promising correlations. It then fully analyzes these correlations
before handing them over to doctors.

#AI algorithms have an insatiable appetite for #data, since the more they
crunch, the more accurate their results will be. Avalue Technology Inc. and
Biomdcare Corporation via @insightdottech


ANALYZING MEDICAL IMAGES WITH AI

Ironically, given the enormous amount of data it must sort through, one of the
main problems Biomdcare encountered was having a small amount of relevant data
to work with.

There are several reasons for this. The first is that AI algorithms have an
insatiable appetite for data, since the more they crunch, the more accurate
their results will be. And unlike the products and machinery AI systems were
originally designed to analyze, humans are unique, their genetic data imbued
with exponentially more permutations.

“In a factory, a lot of data and photos are generated about defective items and
problems on the production line every day. But in healthcare, we don’t have as
much data compared to the overall size of the patient population. Low data
counts are always a problem for AI solutions in healthcare,” explains Richard
Lin, Marketing Director of Biomdcare.

Diseases involving genetic mutations are especially complex, and analyzing them
is never a straightforward task. The problem is compounded for muscular
dystrophy, a rare disease roughly estimated to affect fewer than four per
100,000 people globally, according to the National Institutes of Health. In
addition, its variants are so different from one another that medical
organizations often refer to the condition as a group of diseases.

These issues would normally make it difficult to find enough specific
correlations to confidently point toward a diagnosis. But Biomdcare developed a
proprietary process to make it work. “We use a smaller database to generate a
more accurate AI model. It’s a key value of our solution,” Lin says.

After the data has been analyzed, a report is prepared for physicians and
stored—along with all the genomic data and the patient’s records—on Avalue
servers. Medical professionals can access it on a software platform containing
simple annotation and workflow tools, which they use to arrive at a final
diagnosis.


SMART HEALTHCARE SOLUTIONS HELP PATIENTS AND RESEARCHERS

In addition to making doctors’ work easier, the muscular dystrophy screening kit
provides a faster way for patients to learn if they have the disease. “In
Taiwan, about 1 in 40 patients may have the recessive gene but are without
symptoms,” says Olivia Wang, Product Manager at Biomdcare.

People who have a family member with the disease or couples starting a family
can use the screening kit to learn if they have it or are carriers. Though there
is currently no cure, treatments have been developed to improve muscle strength,
and in some cases, slow disease progression. A faster diagnosis enables patients
and their families to prepare for the future and seek treatment options sooner.

As more people use the screening kit, the data it produces will expand medical
repositories, furthering research and helping AI systems achieve even better
accuracy. “We hope our solution will help the research community build more
reference data to help other patients who need these tests,” Wang says.

The companies are also extending their technology to screen for other diseases,
including breast cancer and human papillomavirus. “We believe AI model-training
solutions can help in many situations,” Lin says. “We anticipate developing more
disease screening solutions in the future.”

 

Edited by Georganne Benesch, Associate Editorial Director for insight.tech.


ABOUT THE AUTHOR

Teresa Meek is an independent content marketing writer for enterprises in the
technology, finance, and healthcare sectors. She has published ghostwritten
executive thought leadership articles, white papers, blog posts, customer
stories, and video scripts for companies including Microsoft, Intel, Google,
JPMorgan Chase, PwC, Humana, and the Cleveland Clinic. More information and
writing samples are available at www.TeresaMeek.com.

Follow on Twitter Follow on Linkedin Visit Website More Content by Teresa Meek
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