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Key Features

  • Confidence bar
  • Detection of up to 130 findings (acute and chronic) on brain CT
  • Normal/abnormal differentiation
  • Notification
  • Visualisation
  • Worklist triage

General Information

Product name

Annalise Enterprise CTB

Subspeciality

Neuro

Modality

CT

Disease targeted

130 findings present in the emergent, urgent, and non-urgent care settings including: Intracranial haemorrhage, haematomas (acute subdural/extradural), fractures, pneumocephalus, infarct, mass effect & midline shift, obstructive hydrocephalus, Intraaxial mass, transependymal oedema, colloid cyst, vasogenic oedema, and many others.

Main task

Not specified

Technical Specifications

Population

All non-contrast head CT studies for patients over 18 years of age

Patient population age

Not specified

Input

Non-contrast CT

Input format

DICOM

Output

Indication of presence of finding, segmentation overlay, confidence and threshold score/bar

Output format

Customizable AI Viewer. DICOM SC. Worklist – HL7 or API based output for worklist triage (prioritisation)

Integration

Integration in standard reading environment (PACS), Integration RIS (Radiological Information System), Integration via AI marketplace or distribution platform, Stand-alone third party application

Deployment

Cloud-based, Hybrid solution, Locally on dedicated hardware, Locally virtualized (virtual machine, Docker)

Trigger for analysis

Automatically, Right after the image acquisition

Processing time

1 - 10 minutes

Regulatory Information

CE Certification

Pathway:

MDR

Class:

Class IIb

Verified by Health AI Register
FDA Certification

Pathway:

510(k) cleared

Class:

Class II

Verified by Health AI Register

Other certifications

Not specified

Market Presence

On market since

07-2022

AI Platforms

Blackford Analysis, Newton's Tree, Nuance, Sectra

Resellers

Not specified

Countries present

40+

Paying clinical customers

300+

Research/test users

10+

Pricing Information

Pricing model

Subscription

Based on

Number of analyses

Evidence & Research

Peer-Reviewed Papers

Peer-Reviewed

View

Evaluation of an Artificial Intelligence Model for Identification of Mass Effect and Vasogenic Edema on CT of the Head

Peer-Reviewed

View

Effects of a comprehensive brain computed tomography deep learning model on radiologist detection accuracy

Source: vendor | First published: Jun 9, 2024 | Last updated: Jul 21, 2025