qER-CTP | Qure AI

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

Currently available for investigational use

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.

Midline shift and Mass effect

Midline shift and Mass effect - Detects and quantifies midline shift on head CT scans.

Infarcts

Detects infarcts from the time of appearance of a well-defined hypodense lesion — about the same time they can be reliably noted by a radiologist. qER detects and localizes acute and chronic infarcts, including lacunar infarcts.

Cranial fracture

Detects fractures, localizes them with a bounding box on the image, and names the cranial bone(s) affected.

Pneumocephalus

Detects pneumocephalus, or the presence of air in the cranial cavity, an infrequent occurrence associated with head trauma or neurosurgery.

Hydrocephalus

Quantifies brain ventricle volume, and cranial vault volume. Both these volumes and their ratio are presented to the user or pre-filed in the report template, along with the reference range for age and gender.

Intra-cerebral bleeds

Detects, localizes, and quantifies the 5 types of intracranial bleeds, and reports their subtype, anatomic location, and volume. - Subarachnoid hemorrhage - Subdural hemorrhage - Extradural hemorrhage - Intraparenchymal hemorrhage - Intraventricular hemorrhage

Tmax, CBF, CBV, MTT Maps

Generate heatmaps for the perfusion parameters including Tmax, CBF, CBV, MTT

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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Blog

Evidence

Journals

12 Jan 2021

Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation - a cohort study

Conference Presentation

02 Mar 2019

Automated classification of X-rays as normal/Abnormal using a high sensitivity deep learning algorithm

Conference Presentation

28 Feb 2019

Automated Detection and Localization of Pneumocephalus in Head CT scan

Journals

05 Jan 2021

Automated Lateral Ventricular and Cranial Vault Volume Measurements in 13,851 Subjects Utilizing Deep Learning Algorithms

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Automated CT Perfusion tool for Acute Ischemic Stroke (AIS)

Automated Heatmaps of Tmax, CBF, CBV, MTT

Outline and Quantify Core-Penumbra Regions