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预测你未来健康状况的无形警告

更新时间:2019/1/30 20:38:02 来源:纽约时报中文网 作者:佚名

The invisible warning signs that predict your future health
预测你未来健康状况的无形警告

It was a sunny day outside, with a hint of spring in the air. I followed Angela, whose name has been changed to protect her identity, down the corridor towards my consulting room in Melbourne. She’d been my patient for several years, but that morning I noticed her shuffling her feet a little as she walked. Her facial expression seemed a bit flat and I noticed she had a mild tremor.

那是一个阳光灿烂的日子,空气中已有一丝春天的气息。我跟着安吉拉(为了保护隐私,采取了化名)沿着走廊来到我在墨尔本的咨询室。她已经在我这看了好几年病,但那天早上我注意到她走路有点拖着脚,面部表情有点僵硬还伴有轻微颤抖。

I referred her to a neurologist and within a week she was commenced on treatment for Parkinson’s disease, but I kicked myself for not picking up on her symptoms sooner.

我给她介绍了一位神经科医生。虽然不到一周时间她就开始接受帕金森症的治疗,但我后悔自己没有早点发现她的症状。

Sadly, this is a common situation for patients all over the world. They are only diagnosed once they begin to show noticeable signs of illness, the body’s warning signal to doctors that something is wrong. If only disease could be spotted earlier, patients might have a chance of receiving early treatment, with even the possibility of their condition being halted before it begins to set in. New technology is beginning to offer some hope.

可悲的是,对于世界各地的患者来说,这种情况太普遍了。只有当身体出现明显不适时,疾病才能被确诊。身体向医生发出的警告,表明出了问题。如果能够更早地发现疾病,患者就有机会接受早期治疗,甚至有可能在病情开始之前就进行干预。新技术为我们带来了一线希望。

With the help of artificial intelligence, patients and doctors could be alerted to potential changes in their health months, or even years, before symptoms appear.

人工智能可以在症状出现前的几个月甚至几年就告诉患者和医生健康状况可能有所改变。

Futurist Ross Dawson, founder of the Future Exploration Network, predicts a shift from the current model of remedial “sick-care” to a new healthcare ecosystem, focused more on prevention and the tracking of potential health problems before they have a chance to develop.

未来探索网络(Future Exploration Network)创始人、未来学家道森(Ross Dawson)预测,未来的医疗生态系统将从目前的亡羊补牢式转向一种新的医疗生态系统,其重点在预防和追踪潜在的健康问题,而不是坐等疾病恶化。

“Shifting societal attitudes, with increased expectations to live full and healthy lives, are driving these changes,” he says. “This decade, the explosion of new technology and algorithms has given rise to deep learning in artificial intelligence, becoming vastly more effective at pattern recognition than humans.”

他说:“社会态度发生了转变,人们对充实健康生活的期望值也不断提高,这些都推动了这些变化。这十年,新技术和算法的爆炸式发展催生了人工智能领域的深度学习,在模式识别方面比人类有效得多。”

By harnessing AI to track our heart rate, breathing, movement and even chemicals in our breath, the technology has the ability to detect potential health problems at an individual level long before obvious symptoms appear. This could help doctors to intervene or allow patients to change their lifestyle to allay or prevent illness.

通过监测心率、呼吸、运动,甚至呼吸中的化学物质,人工智能能够在症状显现之前就发现潜在的健康问题。这可以帮助医生干预或允许病人改变他们的生活方式,以减轻或预防疾病。

Perhaps most excitingly, these systems can discern patterns that are invisible to the human eye, revealing surprising aspects of how our bodies betray our future health.

这些系统能够识别人眼看不到的东西,揭示出我们的身体透露出的关于未来健康状况的信息。这可能是最令人兴奋的一点。

Windows to your health

健康之窗

Dawson highlights studies in which AI is better able to anticipate people who are likely to suffer heart attacks by constant monitoring of their pulse. One study even pulled out variables that cardiologists had not thought of as having predictive value – a home visit from the GP requested by the patient, for example.

道森提到了一些研究,在这些研究中,人工智能通过不断监测脉搏,能够更好地预测出那些人有罹患心脏病的可能。一项研究甚至提取了心脏病学家认为没有预测价值的变量。例如,病人要求全科医生上门检查。

A recent study by researchers at Google showed that AI algorithms could also be used to predict if someone might suffer a heart attack by looking into their eyes. They trained an AI on retina scans from 284,335 patients. By looking for patterns in the crisscross of blood vessels, the machine learned to spot the tell-tale signs of cardiovascular disease.

谷歌最近的一项研究表明,人工智能算法还可以通过观察眼睛来预测你是否会得心脏病。经过训练的人工智能对284335名患者的视网膜进行了扫描,通过分析纵横交错的血管中隐藏的图案,机器学会了发现心血管疾病的迹象。

Daily movements

日常行为

If Dina Katabi has her way, delays in the diagnosis of genetic disorders and debilitating conditions such as Parkinson’s disease, depression, emphysema, heart problems and dementia will be a thing of the past.

如果卡塔比(Dina Katabi)能够如愿以偿的话,那么对遗传病和帕金森症、抑郁症、肺气肿、心脏病和痴呆症等衰弱性疾病的延迟诊断将成为过去。

She has designed a device that transmits low-power wireless signals through a house. These electromagnetic waves reflect off a patient’s body. Every time we move, we change the electromagnetic field around us. Katabi’s device senses these minute reflections and tracks them, using machine learning to follow a patient’s movements through walls.

她设计了一种传输低功率无线信号的装置。这些电磁波会反射到病人身上。身体的移动会改变我们周围的电磁场。卡塔比的设备能感知这些微小反射,并使用机器学习隔墙跟踪病人的移动。

Katabi describes the wireless signals as “amazing beasts” that go beyond our natural senses. Deploying a device in a patient’s home allows their sleep patterns, mobility and gait to be continuously monitored. It can pick up on their breathing rates – even with multiple people in a room – and detect if someone has a fall. It can monitor their heartbeats and even provide information about their emotional state.

卡塔比将无线信号描述为“神奇的野兽”,超越了我们的自然感官。在病人家中安装一个这样的设备,可以持续监测他们的睡眠模式、活动和步态。即使房间里有很多人,设备也可以监测病人的呼吸频率以及是否有人摔倒。除此之外,通过监控心跳,还能了解病人的情绪状态。

“We don’t see them, but they can complement our current knowledge in almost magical ways,” she says. “Our new device is able to traverse walls and extract vital information which can augment our limited ability to perceive change.”

“电磁波肉眼不可见,但却能以神奇的方式补充我们目前的知识,”她说。“人类感知变化的能力有限,但这种设备能够透过墙壁提取重要信息,增强我们的能力。”

This ability to look for changes in the daily behaviour of patients can provide early clues of something being wrong, perhaps before they even know it themselves.

通过监测病人日常行为的变化可以尽早发现疾病的苗头,有时候甚至先于病人本身。

Many of us already utilise a myriad of gadgets to self-monitor everything from our calorie intake to the number of steps we take each day. Artificial intelligence can play a vital role in helping make sense of all this information.

如今,已经有许多人使用各种小工具监测自己的数据,比如卡路里摄入量和每天走的步数。人工智能可以帮助人们理解这些信息方面,发挥了至关重要的作用。

This ability to predict changes in health could be particularly important as our population grows ever older – according to the United Nations, people aged over 60 will account for a fifth of the global population by 2050.

随着人口老龄化不断加剧,预测健康状况变化的能力尤为重要。根据联合国的数据,到2050年,60岁以上的人口将占全球人口的五分之一。

“More and more elderly people are living alone, burdened by chronic disease, which leads to enormous safety concerns,” says Katabi. She believes her device will allow medical professionals to intervene sooner and potentially ward off medical emergencies.

卡塔比说:“独居老年人越来越多,他们被慢性病折磨,带来一系列严重的安全问题。”她相信设备能帮助医护人员进行更快的干预,并有可能避免抢救的情况。

Face value

面部特征

Artificial intelligence could also use the way we look to help us predict future disease. New research suggests it can pick up on subtle differences in our faces that might be the hallmarks of disease.

人工智能还可以通过分析面部特征预测未来可能得的疾病。新的研究表明,它可以识别出人类面部细微的差异,而这些差异可能正是患病的征兆。

FDNA, a Boston-based startup, has developed an app called Face2Gene that uses something it calls “deep phenotyping” to identify possible genetic diseases from a patient’s facial features. It uses an AI technique known as deep learning, teaching its algorithms to spot facial features and shapes that are typically found in rare genetic disorders such Noonan Syndrome.

总部位于波士顿的初创公司FDNA开发了一款名为Face2Gene的应用,使用了一种被称为“深度表型”的技术,可以通过分析患者的面部特征识别出可能患有的遗传疾病。此应用使用了一种被称为深度学习的人工智能技术,能够编写算法识别出一些罕见遗传病(如努南综合症)所特有的面部特征和形状。

The algorithm was trained by feeding it with more than 17,000 photographs of patients affected by one of 216 different genetic conditions. In some of these disorders the patients develop certain facial hallmarks of their condition, such as in Bain-type intellectual disability, where children have characteristic almond-shaped eyes and small chins. FDNA’s algorithm has learned to recognise these distinctive facial patterns that are often undetectable to human doctors.

该算法通过向其输入17000多张受216种不同遗传条件之一影响的患者的照片进行训练。某些疾病的患者会呈现出特有的面部特征,如患有拜恩型智力障碍的孩子们下巴都较窄、眼睛成杏仁状。FDNA的算法已经学会识别这些人类医生通常检测不到的特有的面部模式。

Tests of Face2Gene’s system show that it shortlisted the correct syndrome 91% of the time, outperforming human doctors in spotting patients with conditions such as Angelman syndrome and Cornelia de Lange syndrome.

Face2Gene的测试结果显示,症状的检出率为91%,在检测安吉尔曼综合征和多毛发育障碍综合征表现优于人类医生。

Early diagnosis of rare genetic syndromes like these means that medical treatments can be delivered more promptly – while sparing families the diagnostic odyssey that identifying these conditions often involves.

罕见遗传病的早期诊断意味着可以更快地为患者提供医疗服务,同时使家庭不必经历漫长的诊断过程。

With rare diseases affecting an estimated 10% of the world’s population, AI tools such as these are likely to change the face of medicine.

罕见疾病影响着约10%的世界人口,诸如此类的人工智能技术可能会改变医学界。

Inside your brain

大脑内部

Not all illnesses are obvious from the outside, however. Doctors and surgeons have long relied on X-rays and scans to help them diagnose the reason for their patients’ symptoms. But what if it was possible use these scans to spot a disease before it starts to cause problems? Ben Franc is no ordinary radiologist. The professor of clinical radiology at Stanford University is on a quest to unlock the secrets buried inside millions of whole body PET scans that are performed routinely in oncology departments every year. Doctors focus on these scans to determine where cancerous tumours lie, but never analyse them for other unrelated potential risks to their patients’ health. Extracting information from these images could arm doctors with more information about a patient’s disease or even reveal another previously undiagnosed condition.

然而,并不是所有疾病的症状都显而易见。长期以来,医生都依靠x光和扫描进行诊断。但如果能在疾病发作前就能利用扫描及时发现吗?弗兰克(Ben Franc)不是一个普通的放射科医生。这位斯坦福大学(Stanford University)临床放射学教授正致力于解开PET扫描中隐藏的秘密。每年,肿瘤科例行进行的全身PET扫描达数百万次。医生通过这种扫描确定肿瘤的位置,但从不分析它们对患者健康有无潜在危害。从图像中提取的信息可以使医生更加了解病人,甚至查出以前没有发现的疾病。

In a pilot project, Franc is working with a team to study whether changes in brain metabolism that show up in these PET scans can be used to predict if someone might develop Alzheimer’s disease, a condition that affects 10% of people over the age of 65.

弗兰克正与一研究小组合作试点项目,研究PET扫描展示的大脑代谢变化是否可以用来预测罹患阿尔茨海默氏症的可能性。10%的65岁以上老人都受此病影响。

Using AI, they have developed algorithms that are capable of spotting these subtle changes in metabolism, in this case the uptake of glucose in certain areas of the brain, which are thought to occur early on in the development of Alzheimer’s disease. In tests on sets of images from 40 patients it had never seen before, the algorithm was able to detect the disease on average six years before human doctors finally diagnosed them with Alzheimer’s.

利用人工智能,他们已经开发出了能够发现新陈代谢中这些细微变化的算法,在这种情况下,是大脑某些区域对葡萄糖的吸收,这些被认为是阿尔茨海默氏症早期发展的过程。在对40名从未见过的患者的图像集进行测试后,该算法平均能在人类医生最终诊断出他们患有阿尔茨海默氏症之前6年检测出这种疾病。

It raises the prospect of being able to spot this devastating condition years before the symptoms that lead to diagnosis begin to appear.

它提出了在导致诊断的症状开始出现之前数年就能发现这种毁灭性疾病的前景。

“Computers can find associations that would take humans a lifetime,” says Franc. “AI gives us the opportunity to harness the expertise of exposure to millions of cases. This can lead to early diagnosis and hopefully, more timely and effective treatment for patients.”

弗兰克说:“计算机可以找到人类一生都无法找到的联系。人工智能让我们有机会利用接触数百万案例的专业知识,可以尽早确诊,并有望为患者带来更及时有效的治疗。”

And it is not just Alzheimer’s disease. His group of researchers also recently published a paper showing that combining the enormous sets of raw data that come from MRI and PET scans can be used to predict a patient’s subtype of breast cancer as well as their chances of recurrence-free survival. This growing new field is known as radiomics and uses the raw data from scans to identify features that cannot be spotted with the naked eye. There are more than 5,000 different independent imaging features that can be used and AI offers a powerful new way of analysing all of these.

不仅仅是老年痴呆症。他的研究小组最近还发表了一篇论文,该论文显示,结合来自MRI和PET扫描的大量原始数据,可以用来预测患者的乳腺癌亚型及其无复发生存率。这个不断发展的新领域被称为放射组学,它利用扫描的原始数据来识别肉眼无法识别的特征。有5000多种不同的独立成像特征可以使用,人工智能提供了一种分析所有这些特征的强大的新方法。

“Using machine learning, we were able to identify subsets of these features that may be used to make these predictions,” says Franc. Here, he is hoping to find ways of using AI in settings outside the hospital to predict health. He envisages smart toilets, for example, that can look for changes in a person’s urine or faeces in order to predict disease.

弗兰克说:“机器学习能够识别出这些特性的子集从而进行预测。”弗兰克希望人们在医院外也能使用人工智能预测健康。他设想出了一种智能厕所,可以通过检测人的尿液或粪便的变化来预测疾病。

How you speak

说话方式

While scans and images can give clues about our physical health, our mental health remains somewhat harder to diagnose. But mental health conditions are on the rise, currently affecting around 25% of the global population and reaching epidemic proportions in some countries. As a leading cause of disability, this places enormous strain on society.

虽然扫描和成像技术可以提供关于身体健康的线索,但精神健康状况仍然难以评估。全球心理健康状况不断恶化,目前影响着全球约25%的人口,在一些国家已达到流行病的比例。心理疾病是导致残疾的主要原因,给社会带来了巨大的压力。

Machine learning is offering new ways of detecting mental health conditions early by tuning into tell-tale signs hidden in a person’s choice of words, tone of voice and other nuances of language. Ellie, a digital avatar developed by the University of Southern California’s Institute for Creative Technologies, is a virtual therapist who can analyse more than 60 points on a patient’s face to determine if they might be depressed, anxious or suffering from PTSD. How long a person pauses before answering a question, their posture or how much they nod their head all provide Ellie with further clues about the patient’s mental state during the “consultation”.

机器学习为此提供了新方法,能及时发现心理疾病的早期症状。分析用词、语调和其他语言上的细微差别。由南加州大学(University of Southern California)创意技术研究所(Institute for Creative Technologies)开发的艾莉(Ellie)是一名虚拟治疗师,可以分析病人脸上的60多个点,判断他们是否患有抑郁症、焦虑症或创伤后应激障碍。一个人在回答问题前停顿的时长、姿势和点头的次数都能帮助艾莉在“会诊”时进一步了解病人的精神状态。

This way of using machine learning is expected to bring major advances to psychiatric outcomes by “improving prediction, diagnosis, and treatment of mental illness”, says Nicole Marinez-Martin from the Stanford School of Biomedical Ethics, and her colleagues, in a recent article in the Journal of Ethics.

斯坦福大学生物医学伦理学学院的尼科尔(Nicole Marinez-Martin)和她的同事最近在《伦理学杂志》上的一篇文章中说,机器学习有望“改善精神疾病的预测、诊断和治疗过程”,为精神疾病的治疗带来重大进展。

Advances in AI have also produced emotionally-intelligent bots that are able to have natural conversations with humans ­– technology that is enabling far more people access to treatments that are currently limited by the availability of human resources. Wysa, for example, is a bot designed by therapists and AI researchers to help build people’s mental resilience skills by using evidence-based talking techniques such as cognitive behavioural therapy. The idea is for the bot to ask probing questions that help people untangle how they are feeling after a difficult day.

人工智能技术还催生了具有情感的机器人,可以与人类进行自然的对话。这种技术能让更多人接受治疗,而不是像现在这样受到人力资源的限制。Wysa是一款由治疗师和人工智能研究人员共同设计的机器人,旨在利用认知行为疗法等基于证据的谈话技术,帮助人们建立心理应变能力。机器人会问一些深入的问题,帮助忙碌了一天后的人们理清自己的情绪。

Tough decisions

艰难的决定

When all these individual biometric measurements, alongside genetic profiling, are combined, the result could enable the prediction of individual risk factors that could supersede today’s sweeping medical guidelines. In the world of precision medicine, AI could make the annual routine check-up at the doctor anachronistic.

把这些生物计量值和基因图谱结合在一起使预测个体健康风险成为可能,从而能取代当今广泛的医学指导方针。在精准医疗的世界里,人工智能可能会让医生每年例行的体检看起来有些过时。

But how much trust are we willing to put in an algorithm when it comes to our lives? A recent article in the AMA Journal of Ethics poses a scenario where machine learning is used to make decisions by predicting a patient’s end of life choices. The authors point out that “an algorithm will not lose sleep if it predicts with a high degree of confidence that a person would wish for a life-support machine to be turned off”.

但当人工智能真正进入生活后,我们又愿意给予这些算法多少信任呢?美国医学协会(AMA)《伦理学杂志》(Journal of Ethics)最近发表的一篇文章中假设了这样一种情景——机器学习被用来预测病人的临终选择。作者指出,“即便算法能高度自信地预测出一个人希望关掉生命维持机器,它也不会因此夜不能寐”。

The question is, do we want something that doesn’t worry about the decisions it makes to be making such important calls?

问题是,我们是否想要一种不用担心做出如此重要的决定的东西?

We might prefer the bedside manner of a human doctor over that of a machine, but in the near future an AI doctor might be able to pick up on issues long before their organic counterpart. By being perfectly tailored to our individual personalities, behaviours and emotions they could give us an early warning that just might save our lives.

相比机器,我们或许还是更喜欢人类医生的临床治疗方式,但在不久的将来,人工智能医生可能会比人类医生更早发现问题。根据个性、行为和情感量身定制的人工智能发出的预警可能会救我们一命。

So, while we might not expect a computer to feel, we may want it to understand what and how we are feeling.

我们不希望计算机像人一样有意识,但又希望它能理解我们的感觉。

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