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Building a System-Wide Stroke Program with AI

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Rapid ICH and Rapid Hyperdensity

Identify Suspected Intracranial Hemorrhage and Hyperdensity Volume in Minutes

Rapid ICH and Rapid Hyperdensity use the latest in artificial intelligence technology to quickly triage non-contrast CT (NCCT) cases by immediately notifying clinicians of possible intracranial hemorrhage (ICH) and then providing identified hyperdense regions of the brain. These clinical modules help stroke and trauma teams on the front lines of patient assessment streamline decision making and expedite patient care.


Clear Results to Help Save Lives

Rapid ICH and Rapid Hyperdensity present clear, easy-to-read results that make it easier for physicians to make critical decisions faster. Rapid ICH automatically processes CT images and assesses them for hemorrhage including intraparenchymal (IPH), intraventricular (IVH), subdural (SDH), and subarachnoid (SAH) subtypes; while Rapid Hyperdensity automatically identifies elevated hyper dense regions with a corresponding volume output. Together these modules empower physicians to quickly assess the severity of injury in patients with acute neuro conditions such as traumatic brain injury and hemorrhagic stroke.

Sensitivity of 98.1% and Specificity of 99.7%

Rapid ICH uses the latest in artificial intelligence to quickly and automatically identify suspected intracranial hemorrhages with a sensitivity of 98.1% and specificity of 99.7%.

Quick Assessment of Hyderdense Tissue

Powered by AI, Rapid Hyperdensity uses NCCT scans to quickly assess the volume of hyperdense tissue in the brain to help physicians identify the extent of an intracerebral hemorrhage which is crucial for patient treatment decisions.

Identifies Suspected Hemorrhage Subtypes

Rapid ICH identifies suspected hemorrhage subtypes including intraparenchymal (IPH), intraventricular (IVH), subdural (SDH), and subarachnoid (SAH); excluding hemorrhagic transformations (HTs).

Faster Treatment and Triage Decisions

Within 1 minute from receipt of scan, Rapid ICH identifies suspected intracranial hemorrhages. Rapid Hyperdensity then automatically detects hyperdense regions and provides a corresponding volume output.


Optimized for Frontline Assessment, Rapid ICH and Rapid Hyperdensity are:


In 1 minute or less, Rapid ICH automatically suspects intracranial hemorrhage and immediately notifies clinicians. Rapid Hyperdensity then automatically provides volume output of elevated hyper dense regions.


Leveraging intelligence gained from thousands of scans analyzed from hospitals around the world, Rapid ICH is the most accurate product available today with a sensitivity of 98.1% and a specificity of 99.7% -- considerably reducing notification fatigue caused by false positives.


Rapid ICH automatically sends notifications through the Rapid mobile and web apps, PACS, and email. In Rapid apps, clinicians have the flexibility to specify the type of alerts they receive—positive only or all. Rapid Hyperdensity results can also be easily viewed via Rapid apps, PACS and email.

Suspected ICH Identification:

A Technical Overview of Rapid ICH and the Role of Machine Learning

RapidAI has developed a computerized system based on machine learning that automatically screens CT scans for the presence of intracranial hemorrhage (ICH) immediately after the scan acquisition. This technical paper highlights the importance of this development and the role of state-of-the-art AI systems— from methods and data to suspected ICH identification accuracy and performance.

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The Most Comprehensive Stroke Platform

Rapid ICH

Rapid Hyperdensity


Rapid CTA

Rapid LVO

Rapid CTP

Rapid MRI

RapidAI Insights

Rapid Workflow

Rapid NCCT

“…Rapid has been highly accurate… it’s an essential tool.”

Blaise Baxter, MD
Diagnostic Radiologist / Neurointerventional Radiologist

“...The Rapid software may be one of the greatest breakthroughs in our field.”

Michael Frankel, MD
Professor, Department of Neurology, Emory University School of Medicine; Chief of Neurology, Grady Memorial Hospital; and Director, Marcus Stroke and Neuroscience Center

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