{"id":30201,"date":"2018-11-12T22:33:48","date_gmt":"2018-11-13T03:33:48","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/google-ai-predicting-heart-disease-in-the-blink-of-an-eye\/"},"modified":"2018-11-12T22:43:09","modified_gmt":"2018-11-13T03:43:09","slug":"google-ai-predicting-heart-disease-in-the-blink-of-an-eye","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/google-ai-predicting-heart-disease-in-the-blink-of-an-eye\/","title":{"rendered":"Google AI: Predicting Heart Disease in the Blink of an Eye"},"content":{"rendered":"

Background and Current Methods<\/u><\/strong><\/p>\n

New research from Google and its health tech division, Verily, indicates that there may soon be a far easier way to evaluate a patient’s risk of cardiovascular disease, the world’s leading cause of death [1].\u00a0Google researchers have used machine learning to \u201cextract new knowledge from retinal fundus images\u201d in order to predict if a patient is at risk of suffering from a cardiovascular event, such as a heart attack, within five years [2].<\/p>\n

Currently, doctors rely on blood tests to determine a patient\u2019s risk of suffering from a cardiovascular event [3]. Blood tests use invasive methods of drawing blood and require time to test and analyze the results. Google\u2019s research indicates that retinal exams can be used instead, which are less invasive, easier to obtain, and faster to analyze with machine learning.\"\"<\/a><\/p>\n

Google\u2019s Breakthrough Research<\/u><\/strong><\/p>\n

Researchers at Google used retinal fundal images, which show blood vessels at the back of the eye, to predict various risk factors, such as age, gender, blood pressure, and smoking, that influence major adverse cardiac events. The risk factor predictions are then used as inputs to an algorithm to predict the risk of a cardiovascular event [4].\"\"<\/a><\/p>\n

Google researchers used deep learning, a machine learning technique which \u201callows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction\u201d [5]. Data from 284,335 patients was used to train the deep learning algorithm, which then predicted cardiovascular risk factors for nearly 12,025 patients with very high accuracy. For instance, the algorithm \u201cwas able to predict a person’s age to within 3.26 years, smoking status with 71% accuracy, and blood pressure within 11 units of the upper number reported in their measurement\u201d, which a doctor cannot typically predict [2]. The algorithm then used the entire retinal image to determine the association between the image and the risk of heart attack or stroke. The algorithm was able to identify a patient at risk of a cardiovascular event with 70% accuracy, which is competitive with the European SCORE method, currently used to predict risk based on blood tests, at 72% accuracy [2].<\/p>\n

Additionally, the system can generate attention maps that visually highlight how the algorithm is arriving at its conclusion. In the image below, the attention map indicates areas that correlate to various factors in green [6]. This is particularly exciting since it provides insight into which aspects of the retina contributed most to the algorithm, providing a view into a typically opaque process of computing in machine learning. This provides additional information for doctors to better understand and trust the algorithm, since they can see how it is working.\"\"<\/a><\/p>\n

Short and Medium Term Next Steps<\/u><\/strong><\/p>\n

In the short term, Google\u2019s management is focused on validating these preliminary results. The current study is limited since it only used a 45\u00b0 field of view and it used a smaller data set than average for deep learning analysis [7]. The algorithm will be validated and tested using larger data sets in the short term. In the medium term, Google plans to begin initial testing with patients in real-time. This will allow the researchers to continue to feed new images into the algorithm in order to validate its results and more accurately predict a patient\u2019s risk of a cardiovascular event.<\/p>\n

I would recommend that Google evaluate additional methods for obtaining retinal images in the short to medium term. While it will be groundbreaking to eliminate the need for blood testing for cardiovascular evaluation, Google can further eliminate patient barriers by making it easy for patients to take a simple photo with their iPhone to obtain a retinal image. This will allow patients to complete their cardiovascular evaluation from the comfort of their own home, rather than visiting a doctor\u2019s office to obtain a retinal image.<\/p>\n

 <\/p>\n

Open Questions<\/u><\/strong><\/p>\n

    \n
  1. Evaluating patient health using AI and machine learning introduces the risk of hidden machine bias, which may result in incorrect diagnoses. How can Google use AI and machine learning to support medical professionals in their clinical work without relying too heavily on the machine to determine important patient information?<\/li>\n
  2. Machine learning in health care requires a significant amount of data from healthy and unhealthy patients to improve the accuracy of the algorithm. Would you be willing to share your health data with companies like Google in order to support the development of machine learning algorithms for applications in health care?<\/li>\n<\/ol>\n

    (Word count: 762 words)<\/p>\n

     <\/p>\n

    Sources:<\/strong><\/p>\n

    [1]\u00a0“The Top 10 Causes Of Death”. 2018.\u00a0World Health Organization<\/em>. http:\/\/www.who.int\/en\/news-room\/fact-sheets\/detail\/the-top-10-causes-of-death.<\/p>\n

    [2] Loria, Kevin. 2018. “Google Has Developed A Way To Predict Your Risk Of A Heart Attack Just By Scanning Your Eye”.\u00a0Business Insider<\/em>. https:\/\/www.businessinsider.com\/google-verily-predicts-cardiovascular-disease-with-eye-scans-2018-2.<\/p>\n

    [3] “Blood Tests For Heart Disease”. 2018.\u00a0Mayo Clinic<\/em>. https:\/\/www.mayoclinic.org\/diseases-conditions\/heart-disease\/in-depth\/heart-disease\/art-20049357.<\/p>\n

    [4] Poplin, Ryan, Avinash V. Varadarajan, Katy Blumer, Yun Liu, Michael V. McConnell, Greg S. Corrado, Lily Peng, and Dale R. Webster. 2018. “Prediction Of Cardiovascular Risk Factors From Retinal Fundus Photographs Via Deep Learning”.\u00a0Nature Biomedical Engineering<\/em>\u00a02 (3): 158-164. doi:10.1038\/s41551-018-0195-0.<\/p>\n

    [5] LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. 2015. “Deep Learning”.\u00a0Nature<\/em>\u00a0521 (7553): 436-444. doi:10.1038\/nature14539.<\/p>\n

    [6] Li, Abner, and Abner Li. 2018. “Alphabet\u2019s Verily Analyzing Retinas W\/ Machine Learning To\u00a0Predict Heart Disease”.\u00a09To5google<\/em>. https:\/\/9to5google.com\/2018\/02\/19\/alphabet-verily-eyes-heart-disease\/.<\/p>\n

    [7] “Google’s AI Uses Retinal Images To Reveal Cardiovascular Risk”. 2018.\u00a0Medscape<\/em>. https:\/\/www.medscape.com\/viewarticle\/893223.<\/p>\n

     <\/p>\n","protected":false},"excerpt":{"rendered":"

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