Automated CT Perfusion tool for Acute Ischemic Stroke (AIS)

Automated CT Perfusion maps & quantification to assist clinicians qualify late-window patients for Mechanical Thrombectomy (MT).

Real-time auto-selection of arterial input function (AIF) to post-process brain CTP, and create accurate perfusion parametric maps to help clinicians qualify patients for Mechanical Thrombectomy for late arriving patients

Automated Heatmaps of Tmax, CBF, CBV, MTT

qER-CTP generates relevant perfusion parametric maps and sends real-time notifications to pertinent clinicians in the stroke workflow to enable care early on.

Outline and Quantify Core-Penumbra Regions

Description- qER-CTP calculates the volume of core as rCBF <30% and penumbra as Tmax >6s. This creates a mismatch ratio, mismatch volume and visual aid by highlighting core and penumbra regions on scans

Generate Indexed Map Overlays of Perfusion Parameters

qER-CTP also generates index maps of Tmax, CBF, CBV, and MTT to help get a view of various thresholds

The Product is not FDA approved/CE marked yet and is currently meant for investigational or research use only

Why choose us?

qER helps doctors by facilitating critical decision-making. By prioritizing scans that need urgent attention, the technology helps clinicians diagnose and treat those patients first. Every minute that the technology saves increases the chances of good long-term outcomes in patients with a stroke or brain hemorrhage.

Qualify more patients for Mechanical Thrombectomy

With automated CT Perfusion maps hospitals can qualify more patients coming beyond 4.5 hours window. Providing key parameters such as salvageable tissue & mismatch ratio assists interventional clinicians & hospitals in performing more MTs which is associated with better patient outcomes

Increase Output Efficiency of Clinicians

Automated CT Perfusion enables more centers to perform CT Perfusion imaging by integrating with any CT machine & assisting clinicians in faster decision-making.

Product Capability

Qure.ai’s deep learning algorithms detect, localise and quantify a growing list of brain pathologies including intra-cerebral bleeds and their subtypes, infarcts, mass effect, midline shift, and cranial fractures.

How it works

qER uses deep learning-based AI solutions to automate the screening of head CT scans for signs of intracranial hemorrhage, cranial fractures, midline shift, mass effect, or infarcts in order to prioritize them for clinical review. Along with the priority flag indicating critical scan, it also produces an overlay highlighting the location and measurements of abnormal findings in the scan and a text report quantifying the volume and extent of the abnormalities.

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