
Avicenna Case Study: Intracranial Hemorrhage
CINA-ICH uses deep learning to identify suspected intracranial hemorrhage and prioritizes those cases in the worklist, dramatically reducing turnaround time for head trauma and stroke patients.
CINA-ICH uses deep learning to identify suspected intracranial hemorrhage and prioritizes those cases in the worklist, dramatically reducing turnaround time for head trauma and stroke patients.
CINA-ICH uses deep learning to identify suspected intracranial hemorrhage and prioritizes those cases in the worklist, dramatically reducing turnaround time for head trauma and stroke patients.
Timely and accurate diagnosis are necessary for the management of acute ICH patients. Unfortunately, 16% of critical findings are never reported to referring clinicians. This drop in communication intuitively impacts patient care, especially those with ICH.
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Use the button below to download your free case study and learn how our approach to validation has improved the number of clinically significant findings in AI software.