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HEALTH

 1. Home
 2. Health
 3. AI Radiology Assistant Helps Underserved Communities


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AI RADIOLOGY ASSISTANT HELPS UNDERSERVED COMMUNITIES

Published Date June 1, 2023 Author Brandon Lewis



Statistics indicate there’s only one radiologist for every 100,000 people in
developing countries, which makes capturing and analyzing X-ray images a major
bottleneck in diagnostic healthcare. And it’s not just a shortage of
professionals at issue: The gap in infrastructure is also a problem. In a
well-equipped US hospital, a radiologist might analyze 200 or more X-rays a day;
in an underfunded rural Indian hospital with less sophisticated equipment,
analyzing 100 X-rays a day is difficult.

And it’s not just X-rays that radiologists must look at. They constantly have to
make tough decisions on how to split their time between modalities such as
X-ray, CT, and MRI, especially when they are asked to prioritize CT and MRI
scans. All of this combined can create quite the backlog of unexamined X-rays.

To address these issues, radiology advancements such as AI-based clinical
decision support (CDS) tools are emerging to help radiologists diagnose X-rays
more quickly without compromising quality.


THE BENEFITS OF CLINICAL DECISION SUPPORT

As the name indicates, a clinical decision support tool is designed to help
clinicians analyze images and make decisions. These tools can take on many
forms, such as rule-based systems, mapping-based systems, productivity, or
automation systems.

Over the past decade, AI-based CDS tools have risen to prominence in virtually
every field of medicine that could benefit from the automated analysis of
electronic health records (EHRs) and other clinical data. This rapid growth has
been driven in part by the reduced cost of using AI to review patient data, as
well as new regulatory guidelines from authorities like the FDA that are
smoothing the path to adoption for CDS broadly and for AI in particular.

But although the costs of AI-assisted imaging have come down dramatically over
the past decade, the technology has remained out of reach for poorer regions.
Part of the problem is that AI radiology solutions have focused on specific
diagnoses such as tuberculosis or cystic fibrosis. To have a full diagnostic
suite, a clinic would need multiple AI solutions, driving up costs.

This focus on specific conditions also limits the tools’ ability to save
radiologists time—particularly when it comes to X-rays. “When a patient takes a
chest X-ray, you don’t know whether he has condition A or condition B,” explains
Mukundakumar Chettiar, Head of the Digital Health Initiative within the medical
business unit at  L&T Technology Services (LTTS). “Chest X-rays are used as a
screening tool, so you don’t necessarily know what you are looking for.”

Over the past decade, #AI-based CDS tools have risen to prominence in virtually
every field of #medicine that could benefit from the automated analysis of
electronic #health records (EHRs) and other clinical #data. @LnTTechservices via
@insightdottech


THE NEED FOR GENERAL-PURPOSE SYSTEMS

LTTS developed Chest-rAI, a general-purpose X-ray CDS tool that aims to provide
a more holistic approach to AI-assisted imaging. Rather than looking for a
particular condition, Chest-rAI examines X-rays for a broad spectrum of
abnormalities and potential biomarkers. The tool covers more than 85% of
diagnoses encountered at a medical institution and has an accuracy rate of over
92%.

To reach these numbers, Chest-rAI leverages a novel deep learning architecture
called Convolution Attention-based sentence Reconstruction and Scoring (CARES).
CARES extracts features from radiological images and generates grammatically and
clinically correct reports describing its findings, according to Chettiar.
Chest-rAI also uses a unique scoring mechanism called the Radiological Finding
Quality Index to evaluate the exact radiological findings, localize them, and
determine the size/severity for each term present in the report.

In addition, Intel® AI Analytics and OpenVINO™ toolkits are used to optimize the
inference pipeline and reduce analysis turnaround time from eight weeks in most
cases to as little as two weeks—and radiologists can access the reports remotely
using a web-based interface. The Intel® Extensions for PyTorch (IPEX) is also
used to optimize performance. This combination of automated reporting, quick
turnaround, and remote access dramatically improves radiologists’ ability to
meet the needs of underserved populations.

“Using the Intel toolkits helped our team speed up inference by 1.84 times and
cut turnaround time by 75%,” says Nandish S., AI Engineer at LTTS. “And it
helped reduce the model size by nearly 40%.”

Because it is highly optimized, Chest-rAI can be deployed in many forms: in the
cloud, in on-premises solutions, or at the edge as an embedded solution. This
gives hospitals the flexibility to adopt the solution however it best suits
their budget and existing infrastructure.

Chest-rAI CDS easily integrates with existing hospital systems and can be used
as a standalone application or part of a larger system. The integration process
is designed for simplicity, allowing the CDS to get up and running in a matter
of days when being tied into existing hospital systems like Picture Archiving
and Communication Systems (PACS) and Radiology Information Systems (RIS).


A SMARTER, MORE AFFORDABLE RADIOLOGY SOLUTION

Over the past decade, AI-based tools have transformed many fields, driving
better outcomes across many applications—breast cancer screening, diabetic
retinopathies, classification of skin lesions, prediction of septic shocks, and
more.

Despite these radiology advancements, radiologist workloads have become a
bottleneck to the healthcare system, especially in developing countries.
Existing tools have been too narrowly focused to meet the needs of broad
screening modalities like X-rays. With the emergence of more general-purpose
tools like LTTS’ Chest-rAI, radiologists now have a tool that not only saves
them time but also allows them to serve a larger population—just what’s needed
in many rural hospitals.
 



This article was edited by Christina Cardoza, Associate Editorial Director for
insight.tech.


ABOUT THE AUTHOR

Brandon brings more than a decade of high-tech journalism and media experience
to his current role as Editor-in-Chief of the electronics engineering
publication Embedded Computing Design. His coverage focuses on artificial
intelligence and machine learning, the Internet of Things, cybersecurity,
embedded processors, edge computing, prototyping kits, and safety-critical
systems, but extends to any topic of interest to the electronic design
community. Brandon leads interactive YouTube communities around platforms like
the Embedded Toolbox video interview series and Dev Kit Weekly hardware reviews,
and co-hosts the Embedded Insiders Podcast. Drop him a line at
brandon.lewis@opensysmedia.com or DM him on Twitter @techielew.

Follow on Twitter Follow on Linkedin Visit Website More Content by Brandon Lewis
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