qER is an FDA 510(k) cleared triage and notification tool that prioritizes head CT scans with critical abnormalities such as a bleed, fracture, mass effect or midline shift on the radiology worklist for priority review.
qQuant for head CT scans is a progression monitoring tool for conditions like traumatic brain injury.
Qure.ai's head CT scan algorithms are based on deep neural networks trained with over 300,000 head CT scans. The algorithms are device-agnostic (work with non-contrast scans from all major CT scan manufacturers) and provide results in under a minute. All Qure.ai products integrate directly with the radiology workflow through the PACS and worklist.
Radiology worklists can prove complex to manage, especially in tele-radiology settings or imaging centers with high scan volume. ‘STAT’ designations assigned when the CT scan is requested don’t always reflect the true degree of urgency. The qER prioritization tool helps reduce onset-to-treatment times for critical scans and meet stroke and trauma reporting standards.
qER provides information about bleed subtypes, and localizes the target abnormalities to faciliate review. qER is offered with a reporting assistance mode that pre-populates radiologist templates with this information.
Qure.ai's deep learning algorithms quantify the volume of intracranial structures and lesions rapidly and precisely. This capability is used by clinicians to track the progress of patients with hemorrhagic stroke, traumatic brain injury or hydrocephalus and by researchers to develop new quantitative outcome measures. 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.
In October 2018, a study validating Qure.ai's head CT scan algorithms was published in The Lancet, evaluating performance on detecting intracranial bleeds, fractures, mass effect and midline shift.
The study measured algorithm accuracy versus a 3-radiologist majority on 500 scans and an additional 25,000-scan validation dataset, showing that qER is able to detect these critical abnormalities with near-radiologist accuracy.
We have made the CQ500 dataset publicly available so that others can test their algorithms and build upon our results. We provide anonymized dicoms and the corresponding radiologist reads for the published validation set.
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.
“It’s one of the best radiology–AI efforts to date, because it widens the deep learning interpretation task to urgent referral of many different types of head CT findings,” says Eric Topol, referring to Qure.ai's algorithms.Read Nature Medicine article
The qER algorithm was validated against radiologist findings on more than 300,000 head CT scans; a representative study of a two-week cohort yielded an impressive area under the curve (AUC), a metric used to evaluate AI models, of 0.96.Read MEDNAX blog article
Teleradiology services provider Medica Group has partnered with Qure.ai to develop AI tools for prioritisation and improved efficiency of radiology scan workload.
The tool will also highlight potentially critical findings to reporters, which can be integrated into their diagnoses.It will be trialed and implemented to augment Medica’s urgent, out-of-hours NightHawk service.Read article in LaingBuisson
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