qER
K200921Qure.ai Technologies · cleared 2020-06-17 · product code QAS · Radiology
Premarket evidence — what FDA accepted
source quote (p.4)
“Qure.ai Head CT scan interpretation software, qER, is a deep-learning-based software device that analyses head CT scans for signs of intracranial hemorrhage, midline shift, mass effect or cranial fractures in order to prioritize them for clinical review. The standalone software device consists of an on-premise module and a cloud module.”
source quote (p.4)
“Qure.ai Head CT scan interpretation software, qER, is a deep-learning-based software device that analyses head CT scans for signs of intracranial hemorrhage, midline shift, mass effect or cranial fractures in order to prioritize them for clinical review. The deep learning analysis module underlying qER consists of a set of 4 independent algorithms. The core component of each algorithm is a pre-trained classification convolutional neural network (CNN) that has been trained to detect a specific abnormality from head CT scan images. Both devices also consist of a module that performs the analysis using a pre-trained artificial intelligence algorithm. Specifically, the subject and predicate device utilize a deep learning algorithm which has been trained on medical images.”
source quote (p.5)
“The core component of each algorithm is a pre-trained classification convolutional neural network (CNN) that has been trained to detect a specific abnormality from head CT scan images. Both devices also consist of a module that performs the analysis using a pre-trained artificial intelligence algorithm. Specifically, the subject and predicate device utilize a deep learning algorithm which has been trained on medical images.”
source quote (p.5)
“For on-cloud processing mode, an on-premise gateway is deployed that interfaces with the HIPAA-compliant cloud server(s) where the analysis is performed.”
Validation studies (1)
Retrospective clinical
n=1,320 scans
endpoints: accuracy of qER at triaging head CT scans; clinical benefit of such triage; Sensitivity; Specificity; AUC; time before the scan was opened by a radiologist in the Standard of Care (TTO); time that qER notification was received (TTN)
Reported performance (3 observations)
source quote (p.10)
“98.53 (97.45 - 99.24)”
source quote (p.10)
“91.22 (88.39 - 93.55)”
source quote (p.11)
“2.11 (1.45 - 2.61)”
Each value carries its own analysis unit and task — never compare or pool across devices. Source: 510(k) summary PDF.
Predicate network
Postmarket — what happened after clearance
- re_clearance
The FDA AI/ML device list shows a newer 510(k) K251610 (decision 2025-09-08) from Qure.ai Technologies for a matching device line ("qER-CTA (v1.0)") — a new clearance for the same line is a change event.
first seen 2026-07-08 · k_number:K251610
- re_clearance
The FDA AI/ML device list shows a newer 510(k) K211222 (decision 2021-07-30) from Qure.ai Technologies for a matching device line ("qER-Quant") — a new clearance for the same line is a change event.
first seen 2026-07-08 · k_number:K211222
Recall and MAUDE counts are product-code-level (reports aren't reliably attributable to one device). Signals are descriptive observables with sources — never a judgment that the device is unsafe or drifting. Snapshot 2026-07-08.
Reimbursement — how devices like this got paid
Not yet tracked — no payment pathway indexed for this clearance (the reimbursement corpus is a growing seed set).