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    前沿的人工智能+医疗的新闻动态

    权威的中国医疗精准化建设新方向

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专家评论

金征宇

中华放射学会主任委员
北京协和医院影像科主任

“进化 转型”

我大约是在一年多两年前开始了解推想科技,让我眼前一亮的是,他们是一群非常年轻的科学家在一起组成了推想科技。他们的组合和中国的市场相结合,我觉得是非常有生命力的。在过去一段时间的合作中,我们的青年的医生和科学家们和他们充分地融为了一体,希望在我们在共同努力下,在未来几年做到最卓越。

刘士远

中华放射学会侯任主任委员
上海长征医院影像科主任

“人工智能的应用”

推想科技的产品在长征医院已经部分上线试用,并已经取得了比较好的使用效果。说起对深度学习的展望,一般说展望,都指的是还没有落地,没有实现的东西叫展望,其实基于深度学习的影像产品应该说是已经是落地了,人工智能在长征医院目前就是一个现在进行时的状态。

夏黎明

全国磁共振组委员
武汉同济医院放射科主任

“人工智能的意义”

通过和推想科技的合作,我体会到人工智能的意义有这么几点:首先是提高医生、科室和医院的效率,从目前的产品来说,计算机看图片的速度比人快得多,这能够直接帮助到我们体查的病人;第二,随着计算机看过的病例越多,样本量越大,它的准确率就越高,这样就能够帮助减少误诊漏诊。

张晓祥

中国卫生信息学会分会主任
武汉同济医院信息中心主任

“人工智能对医疗行业的支持”

武汉同济医院设有多个院区,我们建立了一套整体的信息平台,异元异构了300个子系统。我们其实非常需要人工智能对我们提供支持,我们现在有这么多的分院区,总床位数逐步会扩大到一万张,在这种情况下,我们的医生资源是不够的,包括放射医生,病理医生,我们很希望有自动化的A.I.的系统对我们进行辅助诊断。

前沿研究

Computer Aided Diagnosis of Coronary Artery Calcification (CAC) with Convolutional Neural Networks

Coronary artery calcification (CAC) is a typical marker of the coronary artery disease, which is one of the biggest causes of mortality in the U.S. This study evaluates the feasibility of using a deep convolutional neural network (DCNN) to automatically detect CAC in X-ray images. 1768 posteroanterior (PA) view chest X-Ray images from Sichuan Province Peoples Hospital, China were collected retrospectively. Each image is associated with a corresponding diagnostic report written by a trained radiologist (907 normal, 861 diagnosed with CAC)…

Smoking Status Discrimination by MRI Images Based on Deep Learning Method

In this study, we assessed the feasibility of using deep learning techniques to predict smoking status from brain MRI images. We collected head MRI 3D-T1WI images from 127 subjects (61 smokers and 66 non-smokers). There were 176 image slices for each subject. The subjects were aged between 23 and 45, and the smokers had at least 5 years of smoking experience. Twenty-five percent of the subjects were randomly selected as the test set (15 smokers and 16 non-smokers), and the rest of the data were used as the training set...

A Preliminary Examination of the Diagnostic Value of Deep Learning in Hip Osteoarthritis

Hip Osteoarthritis (OA) is a common disease among the middle-aged and elderly people. 8 Conventionally, hip OA is diagnosed by manually assessing X-ray images. This study took the 9 hip joint as the object of observation and explored the diagnostic value of deep learning in hip 10 osteoarthritis. A deep convolutional neural network (CNN) was trained and tested on 420 hip X- 11 ray images to automatically diagnose hip OA. This CNN model achieved a balance of high 12 sensitivity of 95.0% and high specificity of 90.7%, as well as an accuracy of…