This barely helps at all in reducing the time to treatment. It still requires a time consuming and expensive scan. But has this been tested for those cases where prior strokes have occurred? When are we going to fund researchers to test out these 17 possibilities to find out which one is the best? Or maybe the Qualcomm Xprize for the tricorder? Does no one have a clue that we need to be following a strategy rather than this stupid scattershot approach? If our stroke associations actually wanted to be useful they would use some of the brainpower of their employees and create a strategy to solve all the problems in stroke. But no, it is more important to put out press releases.
http://www.news-medical.net/news/20150514/Novel-computer-aided-system-developed-for-acute-stroke-detection.aspx
The Hong Kong Polytechnic University (PolyU) has developed a novel
computer-aided detection system for acute stroke using computer
intelligence technology. Reading 80-100 computer images, the system is
able to detect if the patient was struck by ischemic stroke or
haemorrhagic stroke. The detection accuracy is 90%, which is as high as
that conducted by specialists, but at a much reduced time from 10-15
minutes to 3 minutes. The new system serves as a second opinion for
frontline medical doctors, enabling timely and appropriate treatment for
stroke patients.
Providing treatment to acute stroke patients within the golden hours
of stroke treatment, i.e., 3 hours of stroke onset, is vital to saving
lives. However, stroke specialists do not work around the clock,
increasing the risk of misdiagnosis and delayed diagnosis of acute
stroke. This novel system which analyses brain scans could help save
lives by assisting non-specialists in diagnosis by providing them a
second opinion. Timely diagnosis and treatment within 3 hours of stroke
onset also minimise damage.
Developed
by experts from the Department of Health Technology and Informatics at
PolyU, the computer-aided detection for stroke (CAD stroke) technology
combines sophisticated calculations, artificial intelligence and
pathology to help medical professionals achieve speedy and accurate
diagnosis.
The first part of the system is an algorithm for automatic extraction
of areas of suspected region of interest. A computed tomography (CT)
scan uses X-rays to take pictures of the brain in slices. When blood
flow to the brain is blocked, an area of the brain turns softer or
decreases in density due to insufficient blood flow, pointing to an
ischemic stroke.
The second part is an artificial neural network to classify region of
interest for stroke. The CAD stroke computer "learns" the defining
features of stroke, and performs automated reasoning. CT scans are fed
into the CAD stroke computer, which will make sophisticated calculations
and comparisons to locate areas suspected of insufficient blood flow.
It detects where the images look "abnormal", and will be highlighted for
doctors' review. Early changes including loss of insular ribbon, loss
of sulcus and dense MCA signs will appear as "abnormalities", helping
doctors determine if blood clots are present. As our system is able to
detect subtle change in density, our system is also able to detect
haemorrhagic stroke which is presented as increase in tissue density.
Equipped with the built-in artificial intelligence feature, the CAD
stroke technology can learn by experience. With every scan passing
through, along with feedback from stroke specialists, the application
will improve its accuracy over time.
The life-saving application can also detect subtle and minute changes
in the brain that would escape the eye of even an experienced
specialist, slashing the chances of missed diagnosis. False-positive and
false-negative cases, and other less serious conditions that mimic a
stroke can also be ruled out, allowing a fully-informed decision to be
made.
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