Technology | Clinical Decision Support | February 14, 2018

FDA Clears First AI-Powered Clinical Decision Support Software for Stroke

Viz.AI Contact application uses artificial intelligence algorithm to analyze CT images for stroke indicators; approval paves way for future computer-aided triage software devices

FDA Clears First AI-Powered Clinical Decision Support Software for Stroke

February 14, 2018 — The U.S. Food and Drug Administration (FDA) announced marketing clearance for Viz.AI’s Contact application, the first artificial intelligence (AI)-based clinical decision support (CDS) solution cleared for sale in the U.S. Viz.AI Contact is designed to analyze computed tomography (CT) results that may notify providers of a potential stroke in their patients.

A stroke occurs if the flow of oxygen-rich blood to a portion of the brain is blocked, also known as an occlusion. According to the Centers for Disease Control and Prevention, stroke is the fifth leading cause of death in the U.S. and is a major cause of serious disability for adults. About 795,000 people in the U.S. have a stroke each year.

“Strokes can cause serious and irreversible damage to patients. The software device could benefit patients by notifying a specialist earlier thereby decreasing the time to treatment. Faster treatment may lessen the extent or progression of a stroke,” said Robert Ochs, Ph.D., acting deputy director for radiological health, Office of In Vitro Diagnostics and Radiological Health in the FDA’s Center for Devices and Radiological Health.

The Viz.AI Contact application is a computer-aided triage software that uses an artificial intelligence algorithm to analyze images for indicators associated with a stroke. These types of algorithms can assist providers in identifying the most appropriate treatment plan for a patient’s disease or condition. The FDA is currently creating a regulatory framework for these products that encourages developers to create, adapt and expand the functionalities of their software to aid providers in diagnosing and treating diseases and conditions.

The Viz.AI Contact application is designed to analyze CT images of the brain and send a text notification to a neurovascular specialist if a suspected large vessel occlusion (LVO) has been identified. The algorithm will automatically notify the specialist during the same time the first-line provider is conducting a standard review of the images, potentially involving the specialist sooner than the usual standard of care in which patients wait for a radiologist to review CT images and notify a neurovascular specialist. The notification can be sent to a mobile device, such as a smartphone or tablet, but the specialist still needs to review the images on a clinical workstation.

The Viz.AI Contact application is intended to be used by neurovascular specialists, such as vascular neurologists, neuro-interventional specialists or other professionals with similar training. The application is limited to analysis of imaging data and should not be used as a replacement of a full patient evaluation or solely relied upon to make or confirm a diagnosis.

The company submitted a retrospective study of 300 CT images that assessed the independent performance of the image analysis algorithm and notification functionality of the Viz.AI Contact application against the performance of two trained neuro-radiologists for the detection of large vessel blockages in the brain. Real-world evidence was used with a clinical study to demonstrate that the application could notify a neurovascular specialist sooner in cases where a blockage was suspected. The Viz.ai LVO Stroke Platform obtained an AUC of 0.91, identifying LVOs and alerting the relevant specialist with 90 percent sensitivity and specificity, and a median scan to notification time of under 6 minutes. In over 95 percent of cases, the automatic notifications demonstrated faster notification of the specialist, saving between 6 and 206 minutes, with an average time saving of 52 minutes.

The Viz.AI Contact application was reviewed through the De Novo premarket review pathway, a regulatory pathway for some new types of medical devices that are low to moderate risk and have no legally marketed predicate device to base a determination of substantial equivalence. This action also creates a new regulatory classification, which means that subsequent computer-aided triage software devices with the same medical imaging intended use may go through the FDA’s premarket 510(k) notification process, whereby devices can obtain marketing authorization by demonstrating substantial equivalence to a predicate device.

For more information: www.viz.ai

Related Artificial Intelligence Content

Technology Report: Artificial Intelligence 2017

Why AI By Any Name Is Sweet For Radiology

VIDEO: Examples of How Artificial Intelligence Will Improve Medical Imaging

VIDEO: Deep Learning is Key Technology Trend at RSNA 2017

Machine Learning Concerns Discussed at RSNA/AAPM Symposium

 

Related Content

Varian Unveils Ethos Solution for Adaptive Radiation Therapy
News | Image Guided Radiation Therapy (IGRT) | September 16, 2019
At the 2019 American Society for Radiation Oncology (ASTRO) annual meeting, being held Sept. 15-18 in Chicago, Varian...
FDA Clears GE Healthcare's Critical Care Suite Chest X-ray AI
Technology | X-Ray | September 12, 2019
GE Healthcare announced the U.S. Food and Drug Administration’s (FDA) 510(k) clearance of Critical Care Suite, a...
iCAD's ProFound AI Wins Best New Radiology Solution in 2019 MedTech Breakthrough Awards
News | Computer-Aided Detection Software | September 09, 2019
iCAD Inc. announced MedTech Breakthrough, an independent organization that recognizes the top companies and solutions...
Imaging Biometrics and Medical College of Wisconsin Awarded NIH Grant
News | Neuro Imaging | September 09, 2019
Imaging Biometrics LLC (IB), in collaboration with the Medical College of Wisconsin (MCW), has received a $2.75 million...
A smart algorithm has been trained on a neural network to recognize the appearance of breast cancer in MR images

A smart algorithm has been trained on a neural network to recognize the appearance of breast cancer in MR images. The algorithm, described at the SBI/ACR Breast Imaging Symposium, used deep learning, a form of machine learning, which is a type of artificial intelligence. Image courtesy of Sarah Eskreis-Winkler, M.D.

Feature | Society of Breast Imaging (SBI) | September 06, 2019 | By Greg Freiherr
The use of smart algorithms has the potential to make healthcare more efficient.
Philips and Fujifilm booths at SIIM 2019.

Philips and Fujifilm booths at SIIM 2019.

Feature | SIIM | September 06, 2019 | By Greg Freiherr
Pragmatism from cybersecurity to enterprise imaging was in vogue at the 2019 meeting of the Society of Imaging Inform
Sudhen Desai, M.D.

Sudhen Desai, M.D.

Feature | Pediatric Imaging | September 04, 2019 | By Jeff Zagoudis
Burnout has become a popular buzzword in today’s business world, meant to describe prolonged periods of stress in the
Heath information technology diagram showing use of cloud storage.
Feature | Archive Cloud Storage | September 04, 2019 | Tyna Callahan
In healthcare, critical systems are being used to deliver vital information and services 24x7x365.
Global Diagnostics Australia Incorporates AI Into Radiology Applications
News | Artificial Intelligence | September 04, 2019
Global Diagnostics Australia (GDA), a subsidiary of the Integral Diagnostics Group (IDX), has adopted artificial...
The CT scanner might not come with protocols that are adequate for each hospital situation, so at Phoenix Children’s Hospital they designed their own protocols, said Dianna Bardo, M.D., director of body MR and co-director of the 3D Innovation Lab at Phoenix Children’s.

The CT scanner might not come with protocols that are adequate for each hospital situation, so at Phoenix Children’s Hospital they designed their own protocols, said Dianna Bardo, M.D., director of body MR and co-director of the 3D Innovation Lab at Phoenix Children’s.

Sponsored Content | Case Study | Radiation Dose Management | September 04, 2019
Radiation dose management is central to child patient safety. Medical imaging plays an increasing role in the accurate...