WOLFRAM CONSULTING SERVICES

Revolutionising Cancer Diagnosis withAI and Machine Learning
Industry: Healthcare

Applications: Image processing, machine learning, interface development

ABOUT THE AID-GI PROJECTThe AID-GI Project (Artificial Intelligence-supported Diagnostics of Gastrointestinal
diseases with video capsule endoscopy) is a collaborative research initiative fundedby the UK Government. The aim is to improve the diagnoses of colon cancer andother gastrointestinal (GI) diseases with machine learning, [1] allowing for more GI
screenings without sacrificing quality to help reduce growing hospital backlogs.
HIGHLIGHTS

400,000

Around 400,000 images areproduced by a video capsuleendoscope on its journey throughthe body.

56%

The amount of time specialists
spend per procedure has been
reduced by 56%.

45,000NHS England carries out around45,000 colonoscopy procedures
every year.

THE CHALLENGE
While traditional colonoscopy proceduresthat utilise tube-mounted cameras are still
considered the gold standard of GI tract
analysis, revolutionary video capsuleendoscopy can offer significant advantages.
However, with the number of patientsrequiring screening and the resulting volumeof images being taken by the capsules,
conducting consistent, high-quality analysis
for all the images created is unachievablewith current manual methods.

Image 1

These pill-sized capsules provide high-quality imaging
and are much more comfortable and less invasive
compared to traditional endoscopy procedures.

[1] Innovate UK, 'Results of Competition: Precision Medicine Technologies: Shaping the Future', Innovate UK, Nov. 2018.

The goal of the AID-GI project is to makeinnovative video capsule endoscopy a reality
by removing the data processing bottleneck
through the use of machine learning–based
automated image analysis. Removing therequirement for specialised doctors andconsultants to be present during theprocedure means the pill-sized camera canbe swallowed at home or at a local medical
practice, relieving the burden on acuteservices and endoscopy units while
extending coverage to underserved areas.

THE APPROACH
The Wolfram Consulting Services team built
CapScan, an integrated tool that assists humanoperators in creating accurate training data to
be used in the machine learning–based,
automated image analysis. Importing,converting and analysing videos from thecapsule can now be achieved easily andaccurately at scale through a simple interface.
The Wolfram Consulting Services team's strong
project management expertise made them an
ideal hub in this collaborative research project
where communication about goals, scope,progress and much more was vital.

Additionally, the Wolfram Language was theperfect platform for a project that required
robust importing, processing andpresentation of information from a variety ofsources and in many different formats.
With a potential global rollout planned,
scalability and security was paramount.
Wolfram's multidisciplinary team couldhandle the medical, data science, interface
development and infrastructure elementsof the project, while providing an outside
perspective and innovative ideas.

The CapScan allows clinicians to import thousands of images and automatically identify any abnormalities.
Operators can manually verify the classifications to provide a diagnosis and improve CapScan’s machine learningalgorithms.

ACHIEVEMENTS
Enabling Health Tech Innovations
The Wolfram Consulting Team developed the CapScan tool, removing the bottlenecks
holding back innovations in GI screening technology. Operators no longer need to browse
through thousands of individual images per patient; they are now able to select a single
image and have matching AI-suggested images returned.

Reducing NHS Backlogs and Improving Healthcare Equality
Now GI tract analysis can be performed quickly and accurately without the need forspecialised doctors and consultants. This allows not only for a greater number ofscreenings in acute care but also in community health care centres, improving access tocare in underserved areas and reducing the burden on hospitals.

Strong Foundations for Continuous Improvement
Images with verified anomalies are added back into the training set for CapScan’s machinelearning algorithm, improving its performance the more it is used. Furthermore, theWolfram Consulting Team have created a strong foundation on which we and othercollaborators can build to further integrate machine learning and automated image
analysis into gastrointestinal diagnostics.

MADE POSSIBLE BY WOLFRAM

Using the Wolfram Language allowed us to easily join up the image processing, machine“learning and data visualisation required to present clinicians with the information theyneeded, together with the tools to create and deploy an easy-to-use interface. Because all ofthese tools are built into the Wolfram Engine and designed to work together, the code required
is quite small and very high-level, making it easy for us to maintain and further develop thetool with future generations of the core AI.

”—Jon McLoone
Director of Technical Communication and Strategy
Wolfram Research Europe

LET’S TAKE YOUR PROJECT TO THE NEXT LEVEL
Find out how the Wolfram Technical Services team can jump-start your project with in-depth
troubleshooting, code optimisation, custom training or production deployment.

+44-(0)1993-883400
Wolfram Research Europe Ltd.

consulting@wolfram.com

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