BriefCase - Intracranial Haemorrhage Triage

Aidoc
Aidoc's intracranial haemorrhage algorithm is a triage algorithm that alerts clinicians to the presence of suspected epidural, subdural, subarachnoid, intraventricular and intraparenchymal haemorrhages. This algorithm is run on non-enhanced CT scans of the head or neck.
Information source: Vendor
Last updated: February 16, 2025

General Information

General
Product name BriefCase - Intracranial Haemorrhage Triage
Company Aidoc
Subspeciality Neuro
Modality CT
Disease targeted Intracranial hemorrhage
Key-features Intracranial haemorrhage triage, prioritisation, notification
Suggested use Before: adapting worklist order,
Before: flagging acute findings,
During: perception aid (prompting all abnormalities/results/heatmaps)

Technical Specifications

Data characteristics
Population Adults or transitional adolescents aged 18 and older
Input Head CT without contrast
Input format DICOM
Output Image review software, desktop application, alert notifications, worklist prioritisation
Output format Annotated images: DICOM Secondary Capture Worklist prioritisation: own application, HL7, API, bespoke integration
Technology
Integration Integration in standard reading environment (PACS), Integration RIS
Deployment Hybrid solution, Cloud based, Locally on dedicated hardware
Trigger for analysis Automatically, right after the image acquisition
Processing time 1 - 10 minutes

Regulatory

Certification
CE
Certified, Class IIa , MDR
FDA 510(k) cleared , Class II
Intended Use Statements
Intended use (according to CE) A radiological computer aided triage and notification software indicated for use in the analysis of nonenhanced head CT images. The device is intended to assist hospital networks and appropriately trained medical specialists in workflow triage by flagging and communication of suspected positive findings of pathologies in head CT images, namely Intracranial Hemorrhage (ICH).

Market

Market presence
On market since 11-2017
Distribution channels Change Healthcare, Merge Merative
Countries present (clinical, non-research use)
Paying clinical customers (institutes)
Research/test users (institutes)
Pricing
Pricing model Subscription
Based on Number of analyses

Evidence

Evidence
Peer reviewed papers on performance

  • Prospective Evaluation of Artificial Intelligence Triage of Intracranial Hemorrhage on Noncontrast Head CT Examinations (read)

  • Decreased Hospital Length of Stay for ICH and PE after Adoption of an Artificial Intelligence-Augmented Radiological Worklist Triage System (read)

  • Utilization of Artificial Intelligence–based Intracranial Hemorrhage Detection on Emergent Noncontrast CT Images in Clinical Workflow (read)

  • Diagnostic Accuracy and Failure Mode Analysis of a Deep Learning Algorithm for the Detection of Intracranial Hemorrhage (read)

  • Active Reprioritization of the Reading Worklist Using Artificial Intelligence Has a Beneficial Effect on the Turnaround Time for Interpretation of Head CT with Intracranial Hemorrhage (read)

  • Analysis of head CT scans flagged by deep learning software for acute intracranial hemorrhage (read)
Non-peer reviewed papers on performance

  • The utility of deep learning: evaluation of a convolutional neural network for detection of intracranial bleeds on non-contrast head computed tomography studies (read)

  • Preliminary Results of Aidoc's Deep Learning Algorithm Detection Accuracy for Pathological Intracranial Hyperdense Lesions (read)

  • Utility of Artificial Intelligence Tool as a Prospective Radiology Peer Reviewer -Detection of Unreported Intracranial Hemorrhage (read)
Other relevant papers

  • A prospective randomized clinical trial for measuring radiology study reporting time on Artificial Intelligence-based detection of intracranial hemorrhage in emergent care head CT (read)