qER-Quant

Qure.ai

qER-Quant's deep learning algorithms quantify the volume of intracranial structures and lesions. Clinicians can use these quantitative measurements to assist with determining the severity of the trauma, lesion or underlying disease, or to assist with the comparison of multiple CT scans.

**qER-Quant is part of qER in Europe. In the United States qER-Quant is available as a separate product.**
Product specifications Information source: Vendor
Last updated: Aug. 23, 2021
General
Product name qER-Quant
Company Qure.ai
Subspeciality Neuro
Modality CT
Disease targeted Hemorrhagic stroke, traumatic brain injury, hydrocephalus
Key-features Quantification of intracranial hyperdensities, midline shift and lateral ventricles
Suggested use During: interactive decision support (shows abnormalities/results only on demand)
Data characteristics
Population All adult head CT scans
Input Plain head CT scans
Input format DICOM
Output Segmentation overlay, table of volumes
Output format DICOM secondary capture, DICOM GSPS, PDF and free text
Technology
Integration Integration in standard reading environment (PACS), Integration RIS (Radiological Information System), Integration via AI marketplace or distribution platform, Stand-alone third party application, Stand-alone webbased
Deployment Locally virtualized (virtual machine, docker), Cloud-based
Trigger for analysis On demand, triggered by a user through e.g. a button click, image upload, etc.
Processing time 10 - 60 seconds
Certification
CE
No or not yet, Not certified
FDA
 510(k) cleared, Class II
Market presence
On market since 08-2021
Distribution channels Nuance PIN, Incepto, Sectra Amplifier, Blackford, GE Healthcare, Siemens, Calantic
Countries present (clinical, non-research use)
Paying clinical customers (institutes) 4
Research/test users (institutes) 2
Pricing
Pricing model Subscription
Based on Number of installations
Evidence
Peer reviewed papers on performance

  • Automated Lateral Ventricular and Cranial Vault Volume Measurements in 13,851 Patients Using Deep Learning Algorithms (read)
Non-peer reviewed papers on performance
Other relevant papers