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.

Saturday, March 6, 2021

Assessment of an Artificial Intelligence Algorithm for Detection of Intracranial Hemorrhage

But is it faster and better than one of these? You don't even specify your timeframe. Time is Brain, you know.

Hats off to Helmet of Hope - stroke diagnosis in 30 seconds   February 2017

 

Microwave Imaging for Brain Stroke Detection and Monitoring using High Performance Computing in 94 seconds March 2017

 

New Device Quickly Assesses Brain Bleeding in Head Injuries - 5-10 minutes April 2017

Ski-Mask Design AIR Coil Offers Whole-Brain Imaging Without Claustrophobia

 The latest here:

Assessment of an Artificial Intelligence Algorithm for Detection of Intracranial Hemorrhage

Samantha E.SeymourHSD12Meredith E.LaQueHSD12Blake A.PetersonBS3Kenneth V.SnyderMD, PhD234MaximMokinMD, PhD5MuhammadWaqasMBBS24YiemengHoiPhD6Jason M.DaviesMD, PhD2347Elad I.LevyMD, MBA234Adnan H.SiddiquiMD, PhD234Ciprian N.IonitaPhD1234
1
Department of Biomedical Engineering, University at Buffalo, Buffalo, NY 14260, US
2
Canon Stroke and Vascular Research Center, Buffalo, NY, 14203, US
3
Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, 14203, US
4
Department of Neurosurgery, University at Buffalo, Buffalo, NY 14203, US
5
Department of Neurosurgery, University of South Florida, Tampa, FL, 33606, US
6
Canon Medical Systems USA Inc., Tustin, CA 92780, US
7
Department of Bioinformatics, University at Buffalo, Buffalo, NY 14214, US

Received 18 January 2021, Revised 26 February 2021, Accepted 27 February 2021, Available online 5 March 2021.

ABSTRACT

Background

Immediate and accurate detection of intracranial hemorrhages (ICHs) is essential to provide a good clinical outcome for ICH patients. Artificial intelligence has the potential to provide this, but assessment of these methods needs to be investigated in depth. This study aimed to assess the ability of Canon’s AUTOStroke Solution ICH detection algorithm to accurately identify patients both with and without ICHs present.

Methods

Data from 200 ICH and 102 non-ICH patients who presented with stroke-like symptoms between August 2016 and December 2019 were collected retrospectively. ICH patients had at least one of the following hemorrhage types: intraparenchymal (n=181), intraventricular (n=45), subdural (n=13), or subarachnoid (n=19). Non-contrast computed tomography scans were analyzed for each patient using Canon’s AUTOStroke Solution ICH algorithm to determine which slices contained hemorrhage. The algorithm’s ability to detect ICHs was assessed using sensitivity, specificity, positive predictive value, and negative predictive value. Percentages of cases correctly identified as ICH positive and negative were additionally calculated.

Results

Automated analysis demonstrated the following metrics for identifying hemorrhage slices within all 200 ICH patients (95% confidence intervals): sensitivity=0.93±0.03, specificity=0.93±0.01, positive predictive value=0.85±0.02, and negative predictive value=0.98±0.01. 95% (245/258) of ICH volumes were correctly triaged while 88.2% (90/102) of non-ICH cases were correctly classified as ICH negative.

Conclusions

Canon’s AUTOStroke Solution ICH detection algorithm was able to accurately detect intraparenchymal, intraventricular, subdural, and subarachnoid hemorrhages in addition to accurately determine when an ICH was not present. Having this automated ICH detection method could drastically improve treatment times for ICH patients.(By how much?)

 
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