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Radiology Incorporating AI, Computational Intelligence, and Radiologists’

Domain Intelligence

Dr. Nancy Agens, Head,

Technical Operations, Pubrica

sales@pubrica.com

In Brief

Radiology is a branch of medicine that

practices image technology to diagnose and

treat various diseases. Diagnostic radiology

and interventional radiology which helps

the radiologists to understand the in-depth

severity of the disease. Diagnostic radiology

includes computed tomography (CT),

mammography, magnetic resonance

imaging (MRI) and magnetic resonance

angiography (MRA), X-ray, ultrasound,

positron emission tomography, nuclear

medicine scans, fluoroscopy. It helps in

diagnosing different types of diseases such

as colon cancer, heart diseases, breast

cancer, diagnose angiography, bone scan,

thallium cardiac stress test, thyroid scan,

chest X-ray

Keywords: Radiology, AI,Interventional

radiology, MRI.

I. INTRODUCTION

On the other hand, an interventional

radiologist uses the diagnosed reports as the

base to treat the disease in any part of the

affect body site through the usage of wires,

catheters and other micro-instruments to

allow a minute incision into the body (BSIR,

2018). This procedure used often to treat

fibroids in the uterus, liver problems,

cancers or tumours treatments using

chemoembolization or Y-90 radio

embolization and ablation with

radiofrequency, microwave ablation, cry

ablation, kidney problems, back pain

through vertebroplasty and kyphoplasty,

treating blockage in the arteries and veins

through

angiography or angioplasty and stent

placement, venous access catheter

placement, such as ports and PICCs,

biopsies from lungs and thyroid obtained

through needle, feeding tube placement,

breast biopsy guided either

by stereotactic or ultrasound techniques and

uterine artery embolization.

As time progresses, medical

technology advances year by year and

resolves most of the mysterious diseases

through advanced diagnostic procedures. In

this present scenario, modern medical

technology has occupied its significant

position in almost all the diagnostic world,

for example implementing artificial

intelligence (AI), computational intelligence

and domain intelligence. Hence, in this

present paper author is trying to focus more

information on the incorporation of AI,

computational intelligence and domain

intelligence in the radiology field.

Artificial intelligence is rapidly

progressing in medicine, particularly in

radiology. It was performed based on

performing tasks on computer systems that

involve human intelligence such as decision

making, visual perception, language

translating and speech recognition

(Pakdemirli, 2019). AI extraordinarily

recognizes complex patterns in imaging data

and provide a quantitative assessment in an

automated fashion. More accurate and

reproducible radiology assessments ae done

when AI integrated into the clinical

workflow as a tool to assist physicians. AI is

an amalgamated technology used in

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radiology and diagnoses diseases like lung

cancer- thoracic imaging, which captures

pulmonary nodules that help in distinguish

between benign or malignant. In

mammography screening AI technically

diagnose and interprets the presence of

breast cancer by identifying micro

calcifications (deposits of a small amount of

calcium in the breast (Hosny et al., 2018).

In the mainstream of radiology,

Computer-aided diagnosis (CAD) is a

rapidly accessing technique used in clinical

work with along with AI in radiology. Since,

two decades CAD system was put in clinical

practice in radiology to diagnose prostate,

breast, lung and colon cancer (ESR, 2019).

To interpret the image diagnosis, the

radiologist uses computer output as a

“second opinion” and obtain assistance for

accurate detection. The incorporation of

computational intelligence or algorithm

usually comprises of incredible stages like

the classification of data using Artificial

Neural Networks (ANN), image feature

analysis and image processing. Temporal

subtraction, an element of CAD which has

been applied for enhancing interval changes

and for suppressing stable structures (e.g.,

typical structures) between 2 successive

radiologic images. In this context, to slow

down the misregistration artefacts on the

temporal subtraction images, it follows a

nonlinear image warping technology which

tries to match the old image with the newer

image developed. Temporal subtraction

procedure was evolved more from chest

radiographs, a method which is mostly

applicable to chest computed tomography

(CT) and also in diagnosing nuclear

medicine bone scans improved diagnostic

accuracy significantly. In 1990 ANN was

initially used for computerized differential

diagnosis of interstitial lung diseases in

CAD, later it was tremendously used in

CAD imaging modalities, position emission

tomography/CT and diagnosis of interstitial

lung nodules in chest radiography. CAD

focuses on picture archiving and

communication systems and will become a

standard of care for diagnostic examinations

in daily clinical work (Shiraishi et al., 2011).

A reinvention of radiology is now

considered fully digital domain, where new

tools, picture archiving and communication

systems (PACS) and digital modalities, were

combined with new workflows and

environments that took advantage of the

tools. Similarly, a new cognitive radiology

domain will appear when AI tools combine

with new human-plus-computer workflows

and environments.

Like all technologies, AI will change

radiology for better and worse at the same

time. PACS and speech recognition have

made radiologists real-time, digital, and

virtual- the price of admission to the

information age. AI enables radiologists to

take advantage of machine learning (ML)

and AI. In the present prototype, radiologists

have the potential to become a foundation of

precision health care and further increase

their value (Fig 1). Since several years,

radiologists have been looking for advanced

improvements to visualize the diagnosed

images in a better manner, but as they were

fell back due to no quantification findings,

they were left short of the precision

medicine goal. AI’s ability to quantify

outcome may provide the bridge between

mere visualization and precision medicine.

AI will automate parts of radiologists’

current jobs, at the same time as it improves

their consistency and quality and potentially

lowers operating costs. This technique will

free up radiologists to become more

productive and to focus on the parts of

radiology that humans do best (Dreyer &

Geis, 2017).

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II. FUTURE ASPECT

Finally, look for AI that interacts

with all clinical data, to expand radiologists’

diagnostic and clinical roles. Hence,

radiologists should actively pursue AI that

augments, and not just automates, what they

do. AI products should empower

radiologists to provide more value, more

efficiently. The research for developing AI

that improves the efficiency of radiology

and gains more value from current

examinations.

REFERENCES

[1] BSIR (2018). What is Interventional Radiology?

[Online]. 2018. British Society of Interventional.

Available from: https://www.bsir.org/patients/what-

is-interventional-radiology/.

[2] Dreyer, K.J. & Geis, J.R. (2017). When Machines

Think: Radiology’s Next Frontier. Radiology.

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[3] ESR (2019). What the radiologist should know about

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& Aerts, H.J.W.L. (2018). Artificial intelligence in

radiology. Nature Reviews Cancer. [Online]. 18 (8).

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http://www.nature.com/articles/s41568-018-0016-5.

[5] Pakdemirli, E. (2019). Artificial intelligence in

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