Ferrum Health partners with best-in-class AI providers offering solutions designed to reduce radiology workload and improve patient care.
Avicenna Case Study: Acute Subarachnoid Hemorrhage
How real-time notification can improve care and efficiency?
Notification for neuro-critical findings from non-contrast computed tomography (NCCT) imaging of the head can range upwards of 4 hours even in emergency room and critical care settings, which can delay and degrade patient management.
A 61-year-old man presented with altered mental status after being found face down following a fall. Upon presentation, he was alert but remained confused. An NCCT of the head was ordered.
Following imaging acquisition, the case was not reviewed until two hours later, which revealed an acute subarachnoid hemorrhage, predominantly involving the suprasellar cisterns. A follow-up CT angiography was recommended to exclude aneurysmal rupture.
The mean time-to-notification of CINA-ICH is 22s. Had this tool been deployed, the radiologist would have been alerted at the time of imaging. Moreover, he could have immediately reviewed the case and ordered a CTA while the patient still was in the CT scanner while also alerting neuro-critical care teams. Not only could this have improved patient care, but this would have also saved resources.
Note: Segmentation is not currently available for clinical use in the United States. Please see Avicenna outputs documents for more information.
Intracranial hemorrhages (ICH) affect over two million people worldwide with a 40-50% patient mortality rate within one month, and 80% disability despite aggressive care. Quick and accurate early diagnosis of ICH may facilitate a prompt therapeutic response, allow fast decision-making, and ultimately improve outcomes.
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.
Interested in deploying CINA-ICH at your health facility?