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对抗蚊子的最新武器:计算机视觉|盖茨原创

编辑:[db:作者] 时间:2024-08-25 07:47:06

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对抗蚊子的最新武器:计算机视觉|盖茨原创

打算性能“瞥见”吗?答案很繁芜。
自从保罗艾伦和我开始梦想用个人电脑做些什么以来,我已经关注打算机视觉领域几十年了。
直到现在,打算机才真正达到能够理解视觉输入的阶段。
只管我们还有很长的路要走,但打算机的视觉能力已经在许多方面彻底改变了我们的生活。
它让自动驾驶汽车成为可能。
它被用来快速准确地读取X光片,它还能让手机将路标从一种措辞翻译成另一种。

最近,我对一种不同的运用特殊感到愉快(这是我年轻时从未想象过会关心的):扫描蚊子的图像。

蚊子是传播疟疾的罪魁罪魁,疟疾每年导致超过60万人去世亡,是盖茨基金会康健领域事情的重点之一。
只管科学家在过去几十年里对蚊子有了很多理解,但有一个难题尤其棘手:如何分辨蚊子。
蚊子大约有3500个不同的种类,个中许多长得很像。
纵然是经由高度演习的昆虫学家,也须要在显微镜下仔细不雅观察几分钟才能准确识别出一种蚊子。

为什么我们关心蚊子的种类?最主要的缘故原由是,不同种类的蚊子会携带不同的疾病,有些蚊子根本不携带任何疾病。
(携带疟疾的蚊子属于按蚊属。
)还有其他差异:有些蚊子在室内叮咬人,而有些则在室外觅食。
有些在薄暮进食,有些则在白天进食。
而且只有雌蚊才叮人——血液为它们供应产卵所需的能量。

所有这些差异意味着我们须要不同的工具来对付不同的蚊子。
例如,室内杀虫剂和蚊帐可以很好地对付紧张在室内叮咬的蚊子。
但对付紧张在室外生活和觅食的蚊子,你还须要采纳其他方法,比如肃清它们在户外繁殖的场所。

幸运的是,打算机视觉的一些新用场正在加速蚊子识别的过程。
它们不仅帮助理解我们的对手,还帮助我们瞄准其弱点,拯救更多生命,并使我们更靠近拔除疟疾的目标。

最令人愉快的创新之一是名为“VectorCam”的运用程序,它可以让经由大略培训的人在几秒钟内识别出蚊子种类。

VectorCam由约翰霍普金斯大学的Soumya Acharya博士及其生物工程师团队开拓,得到了乌干达疟疾防控操持、马凯雷雷大学和盖茨基金会的支持。
利用智好手机、VectorCam运用程序以及连接得手机上的廉价镜头,你只需拍摄蚊子的照片,即可立即识别它的种类。
该运用程序能够区分传播疟疾的不同蚊种,还可以判断蚊子的性别,如果是雌蚊,还能判断它是否最近吸过血或是否已经发育出卵子。
通过进一步优化,VectorCam还能识别传播其他疾病(如登革热和寨卡病毒)的蚊子种类。

要理解VectorCam这样的工具可以产生的影响,不妨看看乌干达。
在这个国家,只有215人卖力网络蚊子(常日是在偏远村落落),将它们带回实验室进行识别,并将数据报告给当地卫生系统。
这些卖力此项事情的职员被称为病媒掌握官员,他们还有很多其他职责,包括监测其他疾病以及与当地医院互助。
这是一项艰巨的事情。

有了VectorCam,当地的卫生事情者只需经由大略培训并配备手机就能进行识别事情,从而让病媒掌握官员可以专注于计策方案和调查事情。
该系统目前正在乌干达的阿朱马尼(Adjumani)和马尤格(Mayuge)地区进行测试,虽然我们还在等待终极结果,但它已经显示出了实用性。

之前在这些地区地区消灭传播疟疾的蚊子的考试测验因见效韶光过长而受阻。
阿朱马尼的官员们曾喷洒过一种杀虫剂,但他们没故意识到当地的蚊子已经对其产生了抗药性。
几个月后,当他们创造该地区的疟疾病例并未减少时,才意识到问题所在。

去年12月,他们换用了另一种杀虫剂,这次他们利用VectorCam来监测结果。
他们急速创造喷洒起了浸染——导致大多数疟疾病例的蚊子数量急剧低落。
更主要的是,VectorCam的数据显示,随着这一蚊种的减少,其他蚊种正在取而代之。
因此,战斗仍在连续,但现在当地的卫生团队可以对他们的事情进行微调,由于他们可以立即知道哪些手段有效,哪些无效。

VectorCam不仅能识别蚊子,还简化了数据网络过程。
在许多地方,蚊虫监测仍旧须要手工填写纸质表格,然后这些表格还需运送并手动输入打算机系统。
当数据终极到达决策者手中时,可能已经延迟了数周乃至数月。
有了VectorCam,数据可以被数字化并汇总,为卫生官员供应最新信息。

在蚊虫监测方面还有其他有前景的发展。
个中之一有一个奥妙的名字“Humbug”——它通过智好手机的麦克风捕捉到蚊子翅膀拍打的声音来识别蚊子种类。
(事实证明,不同种类的蚊子拍打翅膀的速率不同。
)Humbug仍处于早期阶段,但如果成功,它可能会实现更自动化和持续的监测。

当然,理解我们的仇敌只是寻衅的一部分。
我们还须要新的、更好的工具来降服它。
我已经其余写了一篇文章 ,先容拔除疟疾事情的最新进展。

在与蚊子的斗争中,我们终于看清了我们的对手。
我乐不雅观地认为,这些创新将使我们比以往任何时候都更靠近一个没有疟疾的天下。

问题答案: 左边的蚊子属于库蚊属( Culex ),不会传播疟疾。
但其余两种蚊子,即 An. funestus (中间)和 An. gambiae (右边),可以传播疟疾。
专家可以通过不雅观察它们翅膀、腿和其他身体部位的花纹来区分后两种。
很奇妙吧?

The newest weapon against mosquitoes: computer vision

Can computers see? The answer is complicated. I've been following the field of computer vision for decades—ever since Paul Allen and I started dreaming about what you could do with a personal computer—and we're only now reaching the point where they can really understand visual inputs. We still have a long way to go, but the ability of computers to see things is already revolutionizing many parts of our lives. It makes autonomous vehicles possible. It’s used to read x-rays quickly and accurately, and it’s what allows a mobile phone to translate street signs from one language to another.

Lately I’ve been especially enthused about a different application (and one my teenage self never would’ve imagined caring about): scanning pictures of mosquitoes.

Mosquitoes are responsible for spreading malaria, which kills more than 600,000 people every year and is a major focus of theGates Foundation’s health work. Although scientists have learned a lot about them in the past few decades, one challenge has been especially stubborn: telling one mosquito from another. There are around 3,500 different species of them, and many look alike. Even a highly trainedentomologisthas to examine one for several minutes under a microscope to identify it accurately.

Why do we care about mosquito species? Most importantly, because different species can carry different diseases, and some don’t carry any diseases at all. (The ones that carry malaria belong to the genus Anopheles .) There are other differences too: Some bite people indoors, while others feed outdoors. Some dine at dusk while others take their meals during the day. And only females bite—the blood gives them the energy needed to lay eggs.

All this variation means we need different tools for different mosquitoes. For example, indoor insecticides and bednets work well against species that primarily bite indoors. But for the ones that mainly live and feed outside, you’ll need to take other steps too, such as eliminating the outdoor spaces where they breed.

Fortunately, some novel uses of computer vision are supercharging the process of identification. They’re not only helping us know our opponent, they’re helping us target its weak spots, save more lives, and move even closer to eradicating malaria.

One of the most exciting innovations is called VectorCam—an app that lets someone with minimal training identify mosquito species in a matter of seconds.

VectorCam was developed by Dr. Soumya Acharya and his team of bioengineers at Johns Hopkins University, with support from Uganda’smalaria control program,Makerere University, and the Gates Foundation.Using a smartphone, the VectorCam app, and an inexpensive lens attached to the phone, you simply take a picture of a mosquito and get it identified right away. The app can distinguish among the different species that transmit malaria. It can also determine the sex of the mosquito and, if the insect is a female, whether it has recently fed on blood or developed eggs. And with further refinement, VectorCam could identify species that carry other diseases, like dengue and Zika.

To see the impact a tool like VectorCam can have, consider Uganda, where just 215 people in the entire country are responsible for collecting mosquitoes (often in remote villages), taking them back to a lab, identifying them, and reporting the data to local health systems. The people who do this are known as vector control officers, and they have lots of other responsibilities too, including monitoring for other diseases and working with local hospitals. It’s a huge job.

With VectorCam, a local health worker with minimal training and a mobile phone can do the identifying, freeing up the vector control officer to focus on strategic planning and investigative work. The system is being tested in the Adjumani and Mayuge districts of Uganda, and although we’re still waiting for the final results, it’s already proving useful.

Previous attempts to kill malaria-carrying mosquitoes in these areas had been hampered by the length of time it took to see results. Officials in Adjumani were spraying an insecticide, but they didn’t realize that the local mosquitoes had become resistant to it. They only discovered the problem months later, when they saw that malaria cases in the area hadn’t dropped.

Then, last December, they switched to a different insecticide, and this time, they used VectorCam to monitor the results. Right away, they learned that the spraying worked—the number of mosquitoes responsible for most of the malaria cases plummeted. What’s more, the data from VectorCam showed that as this species died off, others took its place. So the fight goes on, but now the local health teams can fine-tune their efforts because they know immediately what’s working and what isn’t.

VectorCam doesn't just identify mosquitoes, it also streamlines the process of collecting data. In many places, mosquito surveillance still involves filling out paper forms by hand, which then have to be transported and manually entered into computer systems. By the time the data reaches decision-makers, it may be weeks or months out of date. With VectorCam, the data is digitized and aggregated, giving health officials up-to-date information.

There are other promising developments in mosquito surveillance. One has the clever name ofHumBug—an app that identifies mosquito species by the sound of their wing beats, captured by the microphone in a smartphone. (It turns out that different species flap their wings at different speeds.) HumBug is still in the early stages, but if it proves out, it could allow for even more automated and continuous monitoring.

Of course, knowing our enemy is only part of the challenge. We also need new and better tools to defeat it. I’ve written a separate post with an update on the effort to eradicate malaria.

In the battle against mosquitoes, we're finally getting a clear view of our opponent. I'm optimistic that these innovations will bring us closer than ever to a world that is free of malaria.

Quiz answer: The mosquito on the left is from the genus Culex , which can’t carry malaria. But the two others, An. funestus (in the middle) and An. gambiae (on the right), can. An expert could tell the latter two apart by looking at the patterns on their wings, legs, and other body parts. Tricky, isn’t it?

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