CuraRad-ICH
Overview
CuraRad-ICH is an AI-powered software workflow tool developed by CuraCloud. It is designed to aid in prioritizing the clinical assessment of adult non-contrast head CT cases that show features suggestive of acute intracranial hemorrhage (ICH). The software utilizes deep learning algorithms to analyze CT images and identify suspected ICH findings, making case-level output available to a PACS/workstation for worklist prioritization or triage. Its primary goal is to reduce radiologist turnaround time for suspected ICH cases, thereby potentially speeding up emergency care decisions for this urgent condition. The deep learning model was trained and validated using diverse data sources from numerous imaging facilities across the U.S. and China.
Note: According to Tracxn, CuraCloud (based in Seattle, founded 2016) is a deadpooled company as of April 2026. This information reflects the tool’s capabilities and regulatory status when the company was active.
Reviewed by Pouyan Golshani, MD — Interventional Radiologist
Key Features
- Deep learning algorithms for ICH detection
- Computer-aided triage and notification
- Worklist prioritization for PACS/workstations
- Analysis of adult non-contrast head CT scans
- Reduces radiologist turnaround time
- Seamless integration with PACS/VNA and worklist applications
- Average processing time of approximately 20-43 seconds per case
Use Cases
- Prioritizing acute intracranial hemorrhage cases in emergency settings
- Expediting diagnosis of ICH on head NCCT
- Improving radiologist performance in detecting ICH
- Reducing misdiagnosis of ICH
What Physicians Need to Know
CuraRad-ICH is intended to aid in prioritizing the clinical assessment of adult noncontrast head CT cases with features suggestive of acute intracranial hemorrhage. It is a software workflow tool that operates in parallel to standard-of-care workflow and provides PACS-agnostic integration. The automated detection and rapid notification can help expedite the triage process for suspected ICH cases, allowing for faster intervention and potentially improved patient outcomes. Physicians should integrate the AI's findings with their overall clinical assessment, as the software is designed to assist, not replace, clinical judgment.
CuraRad-ICH is designed for integration with PACS workstations for worklist prioritization or triage purposes. It offers PACS-agnostic integration and operates in parallel with existing workflows. The software outputs its findings on a case level to a PACS workstation.
Details
| Category | Neurology AI, Radiology & Imaging AI, Triage & ER/ICU AI |
| Pricing | Unknown |
| Deployment | Cloud-based |
| Compliance | |
| BAA Available | Unknown |
| HIPAA Compliant | Yes AI-estimated |
| FDA Status | Cleared AI-estimated |
| Integrations | |
| EHR | Not specified |
| Specialties | Emergency Medicine, Neurology, Radiology |
What the Web Says
CuraRad-ICH is an AI-powered software developed by CuraCloud that assists in the detection and prioritization of intracranial hemorrhage (ICH) on head CT scans. It uses deep-learning algorithms to analyze non-contrast head CT cases and flag those suggestive of acute ICH, aiming to expedite clinical assessment and improve patient outcomes. The software received FDA 510(k) clearance in April 2020.
Overall: PositiveStrengths
- Aids in prioritizing clinical assessment of adult non-contrast head CT cases with suspected acute ICH.
- Employs deep-learning algorithms for analysis.
- Outputs findings to PACS workstations for worklist prioritization or triage.
- Trained and validated using CT images from over 300 imaging facilities across the U.S.
- Fast processing time, averaging about 20 seconds per case.
- Potential to enhance diagnostic accuracy and expedite treatment for stroke patients.
Limitations
No significant limitations found in public reviews. Consider checking vendor documentation or requesting a demo for a thorough evaluation.
Based on reviews from: AuntMinnie.com, NIH.gov, Innolitics.com, AIHealthcareNow.com, researcher.life, healthaidb.com, semanticscholar.org, researchgate.net, preprints.org
Last updated: 2026-10-29
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