1. 香港理工大学（The Hong Kong Polytechnic University  ）醫療科技及資訊學系成功研發一套電腦輔助系統，能更快偵測病人急性中風。理大醫療科技及資訊學系副系主任鄧福禧表示，一般斷症方法最少10至15分鐘，亦有機會出現漏診；但新系統以電腦斷層掃描影像評估系統，發現系統診斷準確度達90%，每症分析時間只需3分鐘，最細更能徵測到1亳米病變區域。

一般要確診中風，病人要接受電腦斷層掃描檢查，再由醫生肉眼觀察影像，是否有出現血管栓塞或出血情況。理大醫療科技及資訊學系副系主任鄧福禧表示，病人每次檢查會有80至100張影像結果，人手肉眼斷症需時最少10至15分鐘，且過於細小的變異較難察覺，有機會出現漏診情況。

學系研發的新系統透過運算算式，分析80至100張影像後，就腦部密度的高底，抽取懷疑中風的框選範圍，然後會進行人工神經網絡分析，以22個參數計自算評估該些區域是否中風。鄧表示，學系利用26名中風患者及26名健康人士的電腦斷層掃描影像評估系統，簡單來說，影像密度較正常低的區域，有可能是缺血性中風，反之則為充血性中風。新系統診斷準確度就90%，每症分析時間只需3分鐘，最細更能徵測到1亳米病變區域。

不過鄧福禧強調，系統不能取代醫生角色，但可作第二參考，讓非專科前線醫生診斷疑似中風病人時，多一個參考。他希望系統下一步可發展到雲端應用，讓醫生能於手機接收結果。

From：http://hk.apple.nextmedia.com/realtime/news/20150511/53725513

http://www.fx120.net/kuaixun/201505/923862.html

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 minutess to 3 minutes. The new system serves as a second opinion for frontline medical doctor, 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, it 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 lifesaving 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

https://www.polyu.edu.hk/web/en/media/media_releases/index_id_6109.html

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