Thinking Inside the box With AI and ML

A New Kind of 'Insider Intelligence'

Mary Beth Massat; McKenna Bryant

Disclosures

Appl Radiol. 2019;48(1):34-37. 

In This Article

AI in Medical Imaging Systems

At the 2018 edition of the Radiological Society of North America's scientific exhibition in Chicago in December, many companies showcased how they're holistically implementing AI in their diagnostic imaging systems to not only more effectively address wide-ranging clinical conditions, but also to simplify physician workflows and departmental throughput.

Worldwide, more than 50 million people suffer a traumatic brain injury (TBI) each year, and it's estimated that approximately half the world's population will be subject to one or more TBIs during their lifetime. Yet attempts to personalize treatment can be hampered by qualitative approaches to characterize the nature and severity of TBI.

To address this global issue, icometrix has received an additional FDA 510(k) clearance for icobrain, which now also includes icobrain tbi for computed tomography (CT) images. Leveraging deep-learning algorithms, icobrain tbi is the first CT product to be applied clinically to patients with traumatic brain injuries. Icobrain uses deep learning to quantify clinically important metrics, including hyperdense volumes, cisternal compression, and midline shift to better characterize and management of TBI in an acute clinical setting.

"The development of this product quantifying CT scans of TBI patients in routine clinical practice helps in the standardized interpretation of acute non-contrast CT scans," said Bart Depreitere, MD, PhD, neurosurgeon at University Hospital Leuven (Leuven, Belgium). "Up until today, TBI was always assessed with the naked eye, but this is now complemented by AI technology to provide valuable metrics following clinical guidelines for improved patient care."

Other products fuse AI and diagnostic imaging to create a wholly new system. The Aquilion ONE™/GENESIS Edition premium CT system from Canon Medical Systems USA, Inc. features the company's Advanced Integrated Clear-IQ Engine (AiCE), pending 510(k) clearance, a groundbreaking Deep Convolutional Neural Network (DCNN) image reconstruction for CT that uses deep learning technology to differentiate signal from noise so that it can suppress noise while enhancing signal.

The algorithm forges a new frontier for CT image reconstruction with its ability to learn from the high image quality of Model Based Iterative Reconstruction (MBIR) to reconstruct CT images with superior spatial resolution and low contrast detectability, three to five times faster than traditional MBIR.

With AiCE's deep learning approach, thousands of features learned during training help to differentiate signal from noise for improved resolution. AiCE applies a pre-trained DCNN to enhance spatial resolution while simultaneously reducing noise with reconstruction speeds fast enough for busy clinical environments.

"In AiCE, the algorithm looks for patterns of noise and learns what those patterns are doing, then detects and subtracts noise from the image to improve signal, making the images much more pristine," explained Dominic Smith, senior director, CT, PET/CT, and MR Business Units, Canon Medical Systems USA. "With AiCE technology, we haven't just raised the bar, we've set a new standard for image reconstruction in CT."

FUJIFILM Medical Systems U.S.A., Inc. is launching REiLI, pending 510(k) approval, the company's global Medical Imaging and Informatics AI technology initiative. Under the REiLI brand, Fujifilm is developing AI technologies that strongly support diagnostic imaging workflow, leveraging the combination of deep learning in its AI technology with Fujifilm's image processing heritage.

"The REiLI AI platform demonstrates how we're designing the future of radiology workflow using big data to support clinical decisions," said Johann Fernando, PhD, Chief Operating Officer of FUJIFILM Medical Systems. "With just the push of a button, radiologists will be able to apply AI algorithms to a patient study, which will help them make clinical decisions."

Through two strategic partnerships, Guerbet is leveraging AI in its diagnostic and interventional imaging care products.

An ongoing collaboration with IBM Watson Health allows Guerbet to deliver Watson Imaging Patient Synopsis, a radiologist-trained artificial intelligence (AI) tool that extracts relevant patient information, summarized into concise, single-view reports to better inform diagnostic decisions.

"This new software is artificial intelligence based to support clinical decisions for doctors, enabling them to access patient history in one page. It not only collects information and structures it, it also highlights the most important parts. Now the doctor and radiologist can have a clear understanding of the patient without scrolling through pages and pages in the digital record. It's a major development in the decision support system," said Massimo Carrara, Guerbet Vice President for North America.

The company's commercial partnership with Imalogix will allow it to help optimize diagnostic and interventional imaging care. The Imalogix™ Platform gives healthcare organizations the tools to understand and manage the process, quality and safety related to diagnostic imaging and interventional procedures, and meet evolving regulatory standards surrounding radiation dose management with ease.

The commercial partnership combines Guerbet's leadership in diagnostic and interventional imaging with the cloud-based human and machine-intelligence capabilities of Imalogix™ to proactively identify areas that impact care delivery to reduce variability and improve the quality, safety and efficiency of care around radiation dose management.

At RSNA 2018, GE Healthcare unveiled new applications and smart devices built on Edison, a next-generation intelligence platform that helps accelerate the development and adoption of AI technology and empower providers to deliver faster, more precise care. It expands on GE Healthcare's 200+ imaging applications with new technologies to improve scan consistency, help clinicians detect and prioritize acute cases, and extend the lifecycle of devices.

"Edison provides clinicians with an integrated digital platform, combining diverse data sets from across modalities, vendors, healthcare networks and life sciences settings," said Kieran Murphy, President and CEO of GE Healthcare. "Applications built on Edison will include the latest data processing technologies to enable clinicians to make faster, more informed decisions to improve patient outcomes."

As a holistic and integrated digital platform, Edison combines globally diverse data sets from across modalities, vendors, healthcare networks and life sciences settings. Clinical partners will use Edison to develop algorithms, and technology partners will work with GE Healthcare to bring the latest advancements in data processing to Edison applications and smart devices.

"Edison brings together data from many different sources, both inside and outside the hospital infrastructure, from multiple modalities, from multiple vendors, from multiple hospital networks – even clinical group trials and genomics. Edison brings all that data in and packages it in a way that helps clinicians guide diagnosis, speed up treatment, deliver better outcomes and increase patient satisfaction," said David Seda, Chief Marketing Officer, Vice President, Healthcare Digital.

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