What good does this do to get survivors recovered? That's the whole point of stroke research! If you can't explain that you don't belong in stroke!
Send me hate mail on this: oc1dean@gmail.com. I'll print your complete statement with your name and my response in my blog. Or are you afraid to engage with my stroke-addled mind? I would like to know why you aren't solving stroke to 100% recovery, and what is your definition of competence in stroke? Swearing at me is allowed, I'll return the favor.
And you don't know about much faster ways to determine infarct or bleed?
Hats off to Helmet of Hope - stroke diagnosis in 30 seconds; February 2017
Smart Brain-Wave Cap Recognises Stroke Before the Patient Reaches the Hospital
October 2023
And then this to rule out a bleeder.
New Device Quickly Assesses Brain Bleeding in Head Injuries - 5-10 minutes April 2017
The latest here:
Acute Infarct Core Volume Estimation on Noncontrast Computed Tomography With a Deep Learning Algorithm
Abstract
BACKGROUND
A
simplified patient selection paradigm with noncontrast computed
tomography (NCCT) can reduce the time to reperfusion and widen the
eligibility of acute ischemic stroke large vessel occlusions (LVOs) for
endovascular therapy. The objectives of this article are (1) to develop,
train, and internally validate a deep learning algorithm that estimates
baseline infarct core volume (ICV) on NCCT in anterior circulation LVO
patients, and (2) by using an external set, to ascertain how this
algorithm's (aICV‐NCCT) predictive performance compares with Alberta
Stroke Program Early Computed Tomography Score‐NCCT and ICV‐CT perfusion
in its capacity to estimate the final infarct volume established on
diffusion‐weighted magnetic resonance imaging at 24‐ to 48‐hour
follow‐up.
METHODS
In
the first phase, stroke activations with baseline NCCT and CT
angiography were used to train an aICV‐NCCT. The algorithm was then
internally validated using intraclass correlations and Intersection over
Union. In the external set, patients with LVO treated with endovascular
therapy achieving modified Thrombolysis in Cerebral Infarction score
≥2b and available baseline NCCT, CT angiography, and CT perfusion were
included.
RESULTS
A
total of 2858 studies of patients with stroke alerts were used for
training (80%) and internal validation (20%). We obtained a high
correlation (intraclass correlation coefficient, 0.78; CI, 0.73–0.83)
and an acceptable Intersection over Union of 0.24 on the internal
validation set. The external set consisted on 230 patients with an LVO.
When predicting final infarct volume on the external set, our aICV‐NCCT
was similar to ICV‐CT perfusion (intraclass correlation coefficient,
0.50 versus 0.54; P = 0.764) and Alberta Stroke Program Early Computed Tomography Score‐NCCT (rs, −0.41; P = 0.436).
CONCLUSION
In
this study, we developed and validated a deep learning algorithm that
demonstrates an at least equivalent performance to CT perfusion in
estimating core volume on acute stroke imaging studies in patients with
suspected anterior circulation LVO strokes. The algorithm's robust
performance holds significant potential in settings with limited access
to advanced imaging technologies across diverse healthcare environments.
Graphical Abstract
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