Emergency Room

qER by Qure.ai

Head CT Scan Interpretation and Triage Aid

Automatic traumatic brain injury and stroke triage tool

As the use of computed tomography (CT) scans in emergency rooms is growing, 400 000 head CT scans are performed every year in France alone. But the diagnosis of life-threatening conditions in a timely manner is challenging to ER doctors, already overworked. qER, which covers both stroke and traumatic brain injury, provides support to physicians reading head CTs in the emergency care setting and helps radiologists prioritize the most critical patients.

What if an AI solution could detect Head CT abnormalities?

Utilizing Deep Learning revolutionary algorithms, and scientifically-proven performances, qER detects, localises and quantifies a growing list of brain pathologies including intra-cerebral bleeds and their subtypes, infarcts, mass effect, midline shift, and cranial fractures. qER serves as a radiology assistant to augment the fast and accurate detection of abnormalities and thus helps radiologists evolving in a highly constrained environment to optimize hospital workflow and patient classification.

Expected Benefits

  • Streamlining your workflow by prioritizing abnormal studies in the worklist.
  • Increasing your confidence in Head CT interpretation.
  • Decreasing time to generate reports by pre-populating them with findings.
  • Integrating a decision support tool into your workflow – without slowing you down.

What Radiologists are saying

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.

Dr Eric Topol, Scripps, USA

Publications

  1. National trends in use of computed tomography in the emergency department. Kocher, K. E. et al. Ann. Emerg. Med 58, 452–462 (2011).
  2. Development and Validation of Deep Learning Algorithms for Detection of Critical Findings in Head CT Scans. S. Chilamkurthy, et al. https://doi.org/10.1371/journal.pone.0204155
  3. Chilamkurthy, Sasank, Rohit Ghosh, Swetha Tanamala, Mustafa Biviji, Norbert G Campeau, Vasantha Kumar Venugopal, Vidur Mahajan, Pooja Rao, and Prashant Warier. “Deep Learning Algorithms for Detection of Critical Findings in Head CT Scans: A Retrospective Study.” The Lancet 392, no. 10162 (December 2018): 2388–96. https://doi.org/10.1016/S0140-6736(18)31645-3.
  4. Artificial intelligence pinpoints nine different abnormalities in head scans. Emily Mullin. Nature Medicine (2018). 10.1038/d41591-018-00003-4.