Annalise Enterprise CTB Triage-OH
Overview
Annalise Enterprise CTB Triage-OH is a medical device software application that utilizes an artificial intelligence (AI) algorithm to identify suspected obstructive hydrocephalus (OH) findings in non-contrast computed tomography (NCCT) brain scans. This software workflow tool is designed to aid in the triage and prioritization of radiological medical images, making study-level output available to an order and imaging management system for worklist prioritization or triage. The device employs a convolutional neural network trained using deep-learning techniques on extensive datasets, including over 200,000 CT brain imaging studies, annotated by qualified radiologists. It provides both passive and active notifications for suspected OHCP cases, enabling clinicians to review critical studies earlier in the workflow without decreasing the existing priority of other studies. Annalise Enterprise CTB Triage-OH is intended for adjunctive use by trained clinicians qualified to interpret brain CT studies, enhancing diagnostic accuracy and efficiency in radiology and emergency care settings.
Reviewed by Pouyan Golshani, MD — Interventional Radiologist
Key Features
- AI-powered obstructive hydrocephalus detection
- Non-contrast brain CT analysis
- Worklist prioritization and triage
- Passive and active notifications
- Convolutional neural network algorithm
- Deep learning techniques
- Compatible with PACS/RIS
- Aids in diagnostic accuracy
- Enhances workflow efficiency
- Supports rapid clinical decision-making
Use Cases
- Triage and prioritization of non-contrast brain CT studies
- Early detection of obstructive hydrocephalus
- Improving diagnostic accuracy in radiology departments
- Accelerating critical interventions in emergency care
- Optimizing radiology workflow
- Providing a 'second set of eyes' for radiologists
What Physicians Need to Know
Annalise Enterprise CTB Triage-OH is an adjunctive AI tool designed to rapidly identify and prioritize critical findings like obstructive hydrocephalus and various hemorrhages on non-contrast head CT scans. It is intended to enhance workflow efficiency and accelerate care coordination by flagging urgent cases for earlier review, but it does not replace the trained clinician's interpretation or clinical decision-making. Physicians should always review the AI output concurrently with the original images and all other relevant clinical information.
The system is designed to integrate seamlessly with existing image and order management systems, such as Picture Archiving and Communication Systems (PACS) and Radiology Information Systems (RIS). It makes study-level output available to these systems for worklist prioritization and can send active notifications to clinical teams.
Details
| Category | Neurology AI, Radiology & Imaging AI, Triage & ER/ICU AI |
| Pricing |
Contact for pricing
|
| Deployment | Software workflow tool, integrates with image and order management systems (PACS/RIS). |
| Compliance | |
| BAA Available | Yes AI-estimated |
| HIPAA Compliant | Yes AI-estimated |
| FDA Status |
Yes AI-estimated Annalise Enterprise CTB Triage-OH received 510(k) clearance from the U.S. Food and Drug Administration (FDA) on August 15, 2023, for the triage and notification of obstructive hydrocephalus on non-contrast brain CT scans. It also received FDA Breakthrough Device Designation. |
| Integrations | |
| EHR | Not specified |
| Specialties | Emergency Medicine, Neurology, Radiology |
What the Web Says
Annalise Enterprise CTB Triage-OH is an AI-powered tool designed to assist radiologists in identifying suspected intracranial hemorrhage (ICH) on CT brain scans. It aims to improve workflow efficiency and reduce time to diagnosis for critical cases by triaging studies and highlighting potential bleeds, allowing for faster review by a radiologist. The system is intended to be a decision support tool, not a replacement for human interpretation.
Overall: PositiveStrengths
- Accelerates identification of critical cases like intracranial hemorrhage.
- Improves workflow efficiency for radiologists by triaging studies.
- Potentially reduces time to diagnosis for urgent conditions.
- Aids in prioritizing radiologist workload.
- Designed to be user-friendly and integrate into existing PACS.
- Can enhance patient outcomes through earlier intervention.
Limitations
- Relies on human oversight; not fully autonomous.
- Potential for 'alert fatigue' if too many false positives occur.
- Integration challenges with diverse hospital IT infrastructures.
- Cost of implementation and ongoing maintenance.
- Requires validation and trust-building within clinical settings.
- Limited information available on long-term clinical impact studies.
Based on reviews from: Annalise.ai official website, Healthcare IT News, Radiology Today, AuntMinnie.com, PubMed (for related research on AI in radiology), LinkedIn (discussions among healthcare professionals)
Last updated: 2026-07-17
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