多尺度視網(wǎng)膜圖像增強(qiáng)

If you’ve ever been to see an opthamologistst, you’ve probably undergone a routine procedure where a specialist takes a picture of the back of your eye.

如果您曾經(jīng)去看過眼科醫(yī)師，則可能已經(jīng)過了例行程序，即由專家為您的眼后部拍照。

You will not be surprised to hear that retinal images are rather handy for diagnosing eye diseases. However, you may not have expected that they can also provide a lot of insight into a person’s risk of cardiovascular disease. Retinal imaging is a non-invasive way to examine the condition of someone’s blood vessels, which may be indicative of that person’s wider cardiovascular health.

聽到視網(wǎng)膜圖像對于診斷眼部疾病非常方便，您不會感到驚訝。 但是，您可能沒有想到它們也可以提供有關(guān)人患心血管疾病風(fēng)險的大量見解。 視網(wǎng)膜成像是檢查某人血管狀況的一種非侵入性方式，可能表明該人的心血管健康狀況更強(qiáng)。

If you’ve seen one of these retinal images before, you’ll probably be able to point out the optic disc and the various blood vessels (if you haven’t, try sticking “retina image” into Google — or “fundus”, which is the medical term for the back of the eye).

如果您以前看過這些視網(wǎng)膜圖像之一，則可能可以指出視盤和各種血管(如果沒有，請嘗試將“視網(wǎng)膜圖像”粘貼到Google中或“眼底”中，這是眼后的醫(yī)學(xué)術(shù)語)。

A doctor will be able to go one step further by identifying abnormalities and suggesting features that may warrant further investigation or treatment.

通過識別異常并建議可能需要進(jìn)一步檢查或治療的特征，醫(yī)生將可以進(jìn)一步走下去。

However, feed it to a machine and it’ll be able to predict:

但是，將其輸入到機(jī)器中，它將能夠預(yù)測：

• how old you are;
  你幾歲
• your gender;
  您的性別;
• your ethnicity;
  你的種族;
• whether or not you smoke; and even
  你是否吸煙； 乃至
• what you had for breakfast that morning.
  你那天早上吃的早餐。

Okay, so I may have made that last one up — but remarkably, the rest are true. Make no mistake, retinal images are weirdly predictive.

好吧，所以我可能已經(jīng)提出了最后一個建議-但值得注意的是，其余的都是正確的。 毫無疑問， 視網(wǎng)膜圖像具有奇異的預(yù)測性。

眼睛有它 (The eyes have it)

Researchers at Google wrote a 2017 paper setting out an investigation into how deep learning could be used to predict a range of cardiovascular risk factors from retinal images. The paper briefly explains the more traditional approach to medical discovery: first observing associations and correlations between potential risk factors and disease, and only then designing and testing a hypothesis. Ryan Poplin et al then go on to demonstrate how deep learning architectures can pick up these associations by themselves without being told what to look for.

谷歌的研究人員在2017年發(fā)表了一篇論文 ，對如何使用深度學(xué)習(xí)從視網(wǎng)膜圖像預(yù)測一系列心血管危險因素進(jìn)行了調(diào)查。 這篇論文簡要解釋了更傳統(tǒng)的醫(yī)學(xué)發(fā)現(xiàn)方法：首先觀察潛在風(fēng)險因素與疾病之間的關(guān)聯(lián)和相關(guān)性，然后才設(shè)計和檢驗假設(shè)。 然后，Ryan Poplin等人繼續(xù)演示了深度學(xué)習(xí)架構(gòu)如何在不被告知要尋找什么的情況下自行獲取這些關(guān)聯(lián)。

I’m sure we’ve all heard at some point the assertion that certain medical specialists are going to be replaced by AI algorithms that will be able to outperform them at recognising abnormalities in medical images. This research takes things in a slightly different direction — not seeking to outperform doctors at an existing task but to see what new information machines can glean from these particular images.

我敢肯定，我們都在某個時候聽說過這樣的斷言，即某些醫(yī)學(xué)專家將被AI算法所取代，這些AI算法在識別醫(yī)學(xué)圖像異常方面將勝過他們。 這項研究的方向略有不同-不是試圖在現(xiàn)有任務(wù)上勝過醫(yī)生，而是要從這些特定圖像中了解哪些新的信息機(jī)器可以收集信息。

Early on in their research, the team found that their model was remarkably good at predicting variables like age and gender — so much so that they initially thought that it was a bug in the model (Ryan walks us through how the project developed on TWiML talk 112). But as they looked further into things, they discovered that these were real predictions. Not only that, they were incredibly robust ones as well — age, for example, could be successfully predicted with a mean absolute error of 3.26 years.

在研究的早期，團(tuán)隊發(fā)現(xiàn)他們的模型非常擅長預(yù)測年齡和性別等變量-如此之多，以至于他們最初認(rèn)為這是模型中的錯誤 (Ryan引導(dǎo)我們了解了TWiML上的項目開發(fā)方式) 112 )。 但是當(dāng)他們深入研究事物時，他們發(fā)現(xiàn)這些是真實的預(yù)測。 不僅如此，它們也非常健壯-例如，可以成功預(yù)測年齡，平均絕對誤差為3.26年。

A number of other associations were found, and it turned out that the team could obtain better predictive power than their baseline model across all kinds of variables including blood pressure, blood glucose levels and even ethnicity — all risk factors for cardiovascular disease.

還發(fā)現(xiàn)了許多其他關(guān)聯(lián)，結(jié)果表明，該團(tuán)隊在所有變量(包括血壓，血糖水平甚至種族)的所有變量中都比其基線模型獲得更好的預(yù)測能力，所有變量都是心血管疾病的危險因素。

After observing these results, the team reasoned that if this range of cardiovascular risk factors could be predicted so well, then the model may even have predictive power when it came to identifying which patients were most likely to suffer from a major cardiovascular event (eg a stroke or heart attack) in the future. Despite some limitations in their training data, a model trained only on retinal images (so no explicitly given risk factors) was able to achieve an AUC of 0.70 (skim the ROC/AUC section of this article to learn more about AUC as a performance metric) — which becomes especially impressive in comparison to the 0.72 obtained by another existing risk scoring system which makes use of a great deal more input variables.

在觀察了這些結(jié)果之后，研究小組認(rèn)為，如果可以很好地預(yù)測這一范圍的心血管危險因素，那么該模型甚至可以在確定哪些患者最可能患有重大心血管事件(例如中風(fēng)或心臟病發(fā)作)。 盡管在他們的訓(xùn)練數(shù)據(jù)的一些局限性，一個模型中訓(xùn)練只對視網(wǎng)膜圖像(所以沒有明確給出風(fēng)險因素)能夠達(dá)到0.70的AUC(脫脂的ROC / AUC部分這篇文章 ，詳細(xì)了解AUC性能度量)-與另一個現(xiàn)有風(fēng)險評分系統(tǒng)(使用大量輸入變量)獲得的0.72相比，這尤其令人印象深刻。

不只是心靈的窗戶 (More than just windows to the soul)

Photo by Liam Welch on Unsplash 利亞姆·韋爾奇 ( Liam Welch)在Unsplash上拍攝的照片

In the TWiML podcast mentioned earlier, Ryan speculates about a possibile future in which retinal images are taken as vital signs to give a picture of overall patient health instead of just being used to diagnose eye diseases. As we have seen, this isn’t just fantasy — this straightforward and non-invasive procedure could give a much broader snapshot into a patient’s health than we might have previously expected.

在前面提到的TWiML播客中，Ryan推測了一個可能的未來，其中視網(wǎng)膜圖像被視為生命體征，以提供總體患者健康狀況的圖像，而不僅僅是用于診斷眼部疾病。 如我們所見，這不僅是幻想，而且這種簡單，無創(chuàng)的程序可以比我們以前預(yù)期的更廣泛地了解患者的健康狀況。

To conclude — cardiovascular disease remains the leading cause of death across the world, but 80% of premature heart disease and stroke is preventable. Research like the paper discussed above can help us better understand who is at highest risk of cardiovascular disease and how these groups can be best managed — appropriate early interventions could go a very long way to extending and improving the quality of human life.

總而言之，心血管疾病仍然是世界范圍內(nèi)主要的死亡原因，但是80％的心臟病和中風(fēng)是可以預(yù)防的 。 像上面討論的論文這樣的研究，可以幫助我們更好地了解誰最容易患心血管疾病，以及如何最好地管理這些人群-適當(dāng)?shù)脑缙诟深A(yù)措施對延長和改善人類生活質(zhì)量可能有很長的路要走。

積分和更多信息 (Credits and more info)

Andrew Hetherington is an actuary-in-training and data enthusiast based in London, UK.

安德魯·赫瑟靈頓 ( Andrew Hetherington)是英國倫敦的精算師和數(shù)據(jù)愛好者。

• Check out my website.

  查看我的網(wǎng)站 。

• Connect with me on LinkedIn.

  在LinkedIn上與我聯(lián)系。

• See what I’m tinkering with on GitHub.

  在GitHub上查看我正在修補(bǔ)的內(nèi)容。

Paper discussed: R. Poplin et al., “Predicting cardiovascular risk factors from retinal fundus photographs using deep learning,” DOI 10.1038/s41551–018–0195–0, https://arxiv.org/abs/1708.09843v2.

討論的論文：R. Poplin等人，“使用深度學(xué)習(xí)從視網(wǎng)膜眼底照片預(yù)測心血管危險因素”，DOI 10.1038 / s41551–018–0195-0 ， https： //arxiv.org/abs/1708.09843v2 。

Photos by nrd and Liam Welch on Unsplash.

由nrd和Liam Welch 拍攝的Unsplash照片 。

翻譯自: https://towardsdatascience.com/retinal-images-are-weirdly-predictive-888744b4a153

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