Head CT scans are a first line diagnostic modality for patients with head injury or stroke. qER is designed for triage or diagnostic assistance in this setting. The most critical scans are prioritized on the radiology worklist so that they can be reviewed first. It detects critical abnormalities such as bleeds, fractures mass effect and midline shift, localizes them and quantifies their severity.
Product specifications Information source: Vendor
Last updated: Jan. 5, 2021
Product name qER
Company Qure.ai
Subspeciality Neuro
Modality CT
Disease targeted intracranial hemorrhage, infarct, cranial fracture, pneumocephalus, hydrocephalus, midline shift, cerebral atrophy
Key-features brain pathology detection, mobile notifications, worklist prioritization, report generation, traumatic brain injury tracking
Suggested use Before: adapting worklist order, flagging acute findings
During: perception aid (prompting all abnormalities/results/heatmaps), interactive decision support (shows abnormalities/results only on demand), report suggestion
Data characteristics
Population Head CT scans from adults
Input Plain head CT scans
Input format DICOM
Output triage notification, worklist highlight, binary assessment of presence or absence, segmentation overlay, quantification report, draft radiology report
Output format pdf, DICOM structured report, DICOM secondary capture, DICOM GSPS, HL7
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 on dedicated hardware, Locally virtualized (virtual machine, docker), Cloud-based, Hybrid solution
Trigger for analysis Automatically, right after the image acquisition, On demand, triggered by a user through e.g. a button click, image upload, etc.
Processing time 1 - 10 minutes
Certified, Class IIa
510(k) cleared, Class II
Market presence
On market since 05-2018
Distribution channels Nuance, Incepto, Philips IntelliSpace, Sectra Amplifier, Blackford, GE Healthcare, Siemens
Countries present (clinical, non-research use)
Paying clinical customers (institutes)
Research/test users (institutes)
Pricing model
Based on
Peer reviewed papers on performance

  • Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study (read)

Non-peer reviewed papers on performance
Other relevant papers