谷歌用算法拯救视力

教导电脑识别图片是一件多么具有颠覆性意义的事情。然而今早，透过谷歌研究团队发布的JAMA论文，我们看到了这一技术的雏形。这支团队致力于利用卷积神经网络识别人眼视网膜的显微照片。

瓦伦·古尔山、莉莉·彭和他们的同事使用了深度学习算法，研究了128,175幅美国和印度病患的视网膜图像，随后，54名在美国执业的眼科医生对这些图像进行了糖尿病视网膜病变鉴定。糖尿病视网膜病变的病理在于，连接眼睛后部（视网膜）的光敏器官中的微小血管出现坏死。长期的高血糖会损伤血管，导致其出血或渗血。这一病变将引发视觉模糊，并可能造成失明。该风险深深困扰着全球4.15亿名糖尿病患者。

加州大学旧金山分校临床医学科学家、JAMA论文的通讯作者莉莉·彭说：“至少有三名眼科医师对近13万张图片进行了评级，如果病例比较特殊的话，有时候最多会有7名医师参与。然后，我们根据这些级别和这些图片来培训算法。”然后，该团队测试了模型对两个视网膜扫描（共计11,711幅图片）临床验证组的糖尿病视网膜病变进行识别和适当评级的能力。这些图片都已获得了眼科专家的专业鉴定。

总体来说，谷歌算法以较高的敏感度和特异性，检测出了测试图片的糖尿病视网膜病变。莉莉说：“我们基本上证明了该技术的能力与获美国职业认定、给验证组评级的眼科医生的能力旗鼓相当。”

这项实验的重要性何在？糖尿病视网膜病变在早期是可以预防的，但相对来讲，全球各地很少有人能够有机会进行专家筛选。谷歌的算法弥补了这一空白。它能够可靠地在任何地方发挥作用，也可以在任何可工作的平台上使用，例如智能手机或平板。

莉莉表示，“尽管可能需要庞大的计算集群来对这一模型进行培训，但培训之后的模型尺寸并不是那么大，它甚至可以装入移动设备。”事实上，这一点也是谷歌团队正在与印度的一些医院共同解决的问题之一。

谁知道呢？也许在下一代的智能手机上，糖尿病患者就能够扫描自己的眼睛，并获得早期的预警信号。 （财富中文网）

作者：Clinton Leaf

译者：冯丰

审校：夏林

How transformative can it be when you teach a computer to read images? Well, we’re getting an early glimpse of that this morning with the release of a JAMA paper by a team of Google researchers who trained a deep convolutional neural network to read photomicroscopic images of the backs of human eyes.

Varun Gulshan, Lily Peng, and colleagues used a deep learning algorithm to study 128,175 retinal images drawn from patients in the U.S. and India that were later reviewed for diabetic retinopathy (DR) by a group of 54 U.S.-licensed ophthalmologists. DR is a condition in which the tiny blood vessels in the light-sensitive tissue that lines the back of the eye (the retina) deteriorate. Chronic high blood sugar can damage the vessels, causing them to bleed or leak fluid, which distorts vision and can lead to blindness—a risk of profound concern to 415 million people with diabetes around the world.

“The nearly 130,000 images in this development set were graded by at least three ophthalmologists—sometimes up to seven if it was a tricky case—and then we trained an algorithm based upon those grades and those images,” says Google’s Lily Peng, a physician scientist trained at UCSF, who is the corresponding author on the JAMA paper. Then the team tested the model’s ability to identity and properly grade DR on two “clinical validation sets” of retinal scans (11,711 images in all) that had already been expertly characterized by eye specialists.

Overall, the Google algorithm detected DR on the test images with both high sensitivity and specificity. “We basically showed that we are on par with U.S. board-certified ophthalmologists who had graded the validation sets,” says Peng.

Why is this important? Diabetic retinopathy can be prevented if caught early—but relatively few people around the world have access to expert screening. That’s where Google’s algorithm comes in. It can conceivably be put to use anywhere—or anywhere a smartphone or tablet can work.

“While it may take acres of computer farms to actually train the model,” says Peng, “the model itself—once trained—is actually not that big, and can fit on even a mobile device.” That, in fact, is one of the things the Google team is now working on—in concert with some hospitals in India.

Who knows? Maybe in the next generation of smartphones, diabetics will be able to scan their own eyes for an early warning sign.