Deans' stroke musings: Automated emergent large vessel occlusion detection by artificial intelligence improves stroke workflow in a hub and spoke stroke system of care

Changing stroke rehab and research worldwide now.Time is Brain! trillions and trillions of neurons that DIE each day because there are NO effective hyperacute therapies besides tPA(only 12% effective). I have 523 posts on hyperacute therapy, enough for researchers to spend decades proving them out. These are my personal ideas and blog on stroke rehabilitation and stroke research. Do not attempt any of these without checking with your medical provider. Unless you join me in agitating, when you need these therapies they won't be there.

What this blog is for:

My blog is not to help survivors recover, it is to have the 10 million yearly stroke survivors light fires underneath their doctors, stroke hospitals and stroke researchers to get stroke solved. 100% recovery. The stroke medical world is completely failing at that goal, they don't even have it as a goal. Shortly after getting out of the hospital and getting NO information on the process or protocols of stroke rehabilitation and recovery I started searching on the internet and found that no other survivor received useful information. This is an attempt to cover all stroke rehabilitation information that should be readily available to survivors so they can talk with informed knowledge to their medical staff. It lays out what needs to be done to get stroke survivors closer to 100% recovery. It's quite disgusting that this information is not available from every stroke association and doctors group.

Monday, August 30, 2021

Automated emergent large vessel occlusion detection by artificial intelligence improves stroke workflow in a hub and spoke stroke system of care

Flawed research. No measurement of 100% recovery. Until we get survivors in charge most stroke research will fail to be useful and what is needed.

Automated emergent large vessel occlusion detection by artificial intelligence improves stroke workflow in a hub and spoke stroke system of care

http://orcid.org/0000-0003-1979-2848Lucas Elijovich1,2,
http://orcid.org/0000-0002-0039-0016David Dornbos III2,
Christopher Nickele2,
Andrei Alexandrov1,
Violiza Inoa-Acosta1,2,
http://orcid.org/0000-0002-1536-1613Adam S Arthur2,
Daniel Hoit2

Correspondence to Dr Lucas Elijovich, Semmes-Murphey Neurologic and Spine Institute, Memphis, TN 38120, USA; lelijovich@semmes-murphey.com

Abstract

Background Emergent large vessel occlusion (ELVO) acute ischemic stroke is a time-sensitive disease.

Objective To describe our experience with artificial intelligence (AI) for automated ELVO detection and its impact on stroke workflow.

Methods We conducted a retrospective chart review of code stroke cases in which VizAI was used for automated ELVO detection. Patients with ELVO identified by VizAI were compared with patients with ELVO identified by usual care. Details of treatment, CT angiography (CTA) interpretation by blinded neuroradiologists, and stroke workflow metrics were collected. Univariate statistical comparisons and linear regression analysis were performed to quantify time savings for stroke metrics.

Results Six hundred and eighty consecutive code strokes were evaluated by AI; 104 patients were diagnosed with ELVO during the study period. Forty-five patients with ELVO were identified by AI and 59 by usual care. Sixty-nine mechanical thrombectomies were performed.

Median time from CTA to team notification was shorter for AI ELVOs (7 vs 26 min; p<0.001). Door to arterial puncture was faster for transfer patients with ELVO detected by AI versus usual care transfer patients (141 vs 185 min; p=0.027). AI yielded a time savings of 22 min for team notification and a 23 min reduction in door to arterial puncture for transfer patients.(But you don't even know how fast this has to be to get to 100% recovery. Without that knowledge you don't even know what goal to shoot for.)

Conclusions AI automated alerts can be incorporated into a comprehensive stroke center hub and spoke system of care. The use of AI to detect ELVO improves clinically meaningful stroke workflow metrics, resulting in faster treatment times for mechanical thrombectomy.