Thinking Inside the box With AI and ML

A New Kind of 'Insider Intelligence'

Mary Beth Massat; McKenna Bryant


Appl Radiol. 2019;48(1):34-37. 

In This Article

Devices With Edison Include:

AIRx (510(k) pending)—An AI-based, automated workflow tool for MRI brain scanning designed to increase consistency and productivity, AIRx is designed to provide automated slice prescriptions to help reduce previously redundant, manual steps. AIRx features a pre-trained neural network model that leverages deep learning algorithms and anatomy recognition based on a database of over 36,000 images sourced from clinical studies and reference sites.

Critical Care Suite (510(k) pending)—Hosted on the Optima XR240amx mobile X-ray system, a first of its kind AI-embedded imaging device, Critical Care Suite is designed to identify cases of pneumothorax at the point of care to enable prioritization of image review. Critical Care Suite will employ a suite of AI algorithms to identify this potentially life-threatening condition in chest X-Rays with high accuracy (>0.95 AUC). The AI algorithms are designed to share the output through an onscreen notification. When a pneumothorax condition is identified, the point-of-care notification alerts the clinical team, enabling prioritization of image review. The AI results are sent to PACS for review of the critical findings by a radiologist.

Automated Lesion Segmentation on LOGIQ™ E10 ultrasound—This increases productivity through automation, helping eliminate measuring lesions manually by segmenting an identified breast, thyroid or liver lesion and automatically providing a trace of the lesion and corresponding area. This feature helps ensure consistency among different users, or even the same user, for documentation and follow-ups.

CT Smart Subscription—This application provides continuous access to the latest CT software, extending the lifecycle of the device and making it more valuable over time. Applications can be selected based on a hospital or health system's unique needs, with options ranging from intracranial hemorrhage and stroke detection to routine dose reduction and optimization to cardiac function assessment.

"There's a lot of hidden meaning in the deep data, but it takes a significant sophistication to extract the value. AI gives us an opportunity to see patterns that we don't see and change the way we care for patients, which can ultimately improve outcomes," said Rachael Callcut, MD, MSPH, a partner in the development of Critical Care Suite, Associate Professor of Surgery at the University of California San Francisco (UCSF), a surgeon at UCSF Health and Director of Data Science for the Center for Digital Health Innovation.

By leveraging the power of deep learning and cloud supercomputing, Arterys delivers an AI- and Cloud-powered solution for rapid analysis of cardiac MR images. Arterys Cardio AI's newest deep-learning features include: quantitative delayed enhancement segmentation of myocardium and identification of reference points where users can obtain scar quantification and extent enhancement on a 17-segment AHA plot; and semi-quantitative perfusion of the myocardium, identification of reference points and co-registration that users can create a blood pool ROI to obtain signal intensity graphs and semi-quantitative values, such as upslope and time to peak, on a 17-segment AHA plot. Both solutions are for research only in the U.S. due to contrast agents used.

Arshid Azarine, MD, head of cardiovascular imaging in the Department of Radiology at Saint Joseph Hospital in Paris says that with the Arterys solution, his team spends less time with post-processing. "Review of some complex cardiac MR cases that once took an hour or longer with manual processing can now be done in as little as 10–15 minutes. Thanks to powerful cloud computing, we gain more time to address our patients' needs and can more easily collaborate on their care with colleagues and experts from around the world," Dr. Azarine says.

NVIDIA continues to expand its ecosystem of partners to apply AI to healthcare. With the release of the Clara Software Development Kit (SDK), part of the open NVIDIA Clara platform, developers can deploy AI, visualization or any computer-intensive application in any GPU platform they have. Imaging companies such as FujiFilm, United Imaging and Canon have deployed NVIDIA DGX supercomputers as the AI infrastructure to accelerate their development of AI including enhancing the quality of image acquisition.

The works-in-progress CT CoPilot from HealthLytix, a precision health company and developer of quantitative medical imaging solutions that leverage machine learning, integrates into existing workflow to improve radiologist efficiency, measurement accuracy and clinical confidence. The solution automatically generates consistent views of head CT studies and provides automated quantitative measurements and subtraction series to increase conspicuity of changes between exams.

For Nikdokht Farid, MD, Associate Professor of Radiology, Neuroradiology Division, University of California San Diego, CT CoPilot "quickly and automatically provides correctly aligned images, which enables me to read head CT scans faster and more confidently. It has increased my productivity and has become indispensable in my interpretation of head CTs."

According to HealthLytix, studies performed at UCSD using CT CoPilot demonstrated a 23% improvement in reader certainty when determining increased ventricular volume and a 14% improvement when determining decreased ventricular volume. Other related research showed that CT CoPilot reduced the average radiologist interpretation time by 73% when assessing for change in ventricular volume, without sacrificing clinical accuracy.

With a focus on improving medical efficiency and the patient experience, Subtle Medical received FDA clearance for SubtlePET, an AI-powered technology that enables hospitals and imaging centers to enhance images from faster scans. The company says this can improve patient experience during imaging procedures and also boost exam throughput and provider profitability. The technology leverages deep learning algorithms that integrate seamlessly with any OEM scanner and PACS to enhance images during acquisition without any interruption or alteration in the imaging specialists' workflow. It improves the quality of noisy images acquired with shorter scan times.

According to Michael Brant-Zawadzki, MD, FACR, senior physician executive, Hoag Hospital, Newport Beach, CA, "We have been able to dramatically increase PET scan efficiency and provide a superior patient experience. SubtlePET technology allows us to scan a patient four times faster than normal, yet maintain equal image quality, not otherwise impacting work flow. This creates immediate ROI benefit for our hospital and a compelling value proposition."

Several medical imaging companies are working to integrate MaxQ AI's Accipio platform into their CT systems to support an acute care physician's ability to identify and prioritize brain bleed stroke or head trauma. Intracranial hemorrhage detection software, Accipio Ix, will be integrated as a function of GE Healthcare's CT Smart Subscription offering, enabling GE CT users to download the solution to their scanners from the cloud. Samsung NeuroLogica will now offer Accipio IX as an additional tool for its OmniTom mobile 16-slice CT scanner and its mobile stroke unit and other emergency vehicle equipped with a CereTom CT scanner. The company also announced a distribution agreement with the EnvoyAI subsidiary of TeraRecon for Accipio Ix, making it available to TeraRecon's installed base of customers.

Accipio Ix, which received FDA clearance in early November 2018, leverages AI technology to automatically analyze non-contrast head CT images and detect intracranial hemorrhage to identify and prioritize patients with a brain bleed for the treating physician. The solution natively integrates into CT systems and PACS and can be installed on-premise or via a cloud-based download.

"The Accipio platform is not about enabling radiologists to read more, rather it is designed to impact the quality and speed of treatment in cases of acute stroke or hemorrhage," says Gene Saragnese, CEO, MaxQ AI. "How we use AI matters; we need to use it to augment human caregivers and extend clinical resources."