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.
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
- National trends in use of computed tomography in the emergency department. Kocher, K. E. et al. Ann. Emerg. Med 58, 452–462 (2011).
- 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
- 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.
- Artificial intelligence pinpoints nine different abnormalities in head scans. Emily Mullin. Nature Medicine (2018). 10.1038/d41591-018-00003-4.