{"id":48197,"date":"2018-09-17T13:16:32","date_gmt":"2018-09-17T13:16:32","guid":{"rendered":"https:\/\/news.microsoft.com\/?p=418341"},"modified":"2018-09-17T13:16:32","modified_gmt":"2018-09-17T13:16:32","slug":"ai-and-preventative-healthcare-diagnosis-in-the-blink-of-an-eye","status":"publish","type":"post","link":"https:\/\/sickgaming.net\/blog\/2018\/09\/17\/ai-and-preventative-healthcare-diagnosis-in-the-blink-of-an-eye\/","title":{"rendered":"AI and preventative healthcare: Diagnosis in the blink of an eye"},"content":{"rendered":"

In his office in suburban Beijing, Zhang proudly demonstrated the physical part of Airdoc\u2019s system \u2013 a small desktop device that looks similar to a scanner a neighborhood optometrist might use for a routine eye exam.<\/p>\n

You sit on a stool, lean forward, place your chin on a padded brace, and stare into the darkness of an eyepiece. The algorithm then takes over, precisely adjusting the angle of your head until a green cross comes into focus in the gaze of your right eye. A moment later there\u2019s a bright, but not uncomfortable, flash of white light. The process is repeated for your left eye.<\/p>\n

The machine has just taken high-resolution medical-grade images of both your retinas. It instantly sends them to the cloud where it takes 20 to 30 milliseconds (about the same time as an eye blink) of computation to analyze both.<\/p>\n

\"\"
Above: Taking a test at Airdoc\u2019s Beijing office.<\/figcaption><\/figure>\n<\/p>\n

Moments later an impressively detailed diagnostic dashboard is sent to your smartphone. It rates from low to medium to high your susceptibility to a long list of diseases. If there is a problem, it urges you to seek professional medical help.<\/p>\n

Right now, it can search for 30 diseases. More machine learning will soon boost that number to 50, and eventually, it could go beyond 200.<\/p>\n

Zhang regards his system as a gamechanger because of its potential to deliver at scale and relieve stretched medical resources. To date, it has scanned more than 1.12 million people, mostly in China, but also in the United States, India, Britain, and parts of Africa. \u201cAirdoc users are all over the world.\u00a0 We hope our deep learning technology can prevent all kinds of disease.\u201d<\/p>\n

China, with a population of 1.3 billion, only has about 1,100 eye doctors who are qualified to analyze retinal images. So, the challenge of providing adequate diagnostic services is truly massive \u2013 and perhaps no more so than for the epidemic of diabetes.<\/p>\n

Authorities estimate as many as 114 million Chinese have diabetes \u2013 but only 30 percent of them know that. The other 70 percent are unaware and, without early detection, will eventually be struck down with serious maladies, like blindness, strokes and other potentially fatal conditions.<\/p>\n

\u201cDiabetic retinopathy, or DR, is one of the most common and serious complications of diabetes. Once patients feel symptoms, they are already in a severe stage of DR and will go blind without proper treatment,\u201d says Dr. Rui Li Wei (pictured in top image) of Shanghai\u2019s Changzheng Hospital, one of several major medical institutions that now routinely uses Airdoc\u2019s technology as a quick, accurate, and simple diagnostic tool.<\/p>\n","protected":false},"excerpt":{"rendered":"

In his office in suburban Beijing, Zhang proudly demonstrated the physical part of Airdoc\u2019s system \u2013 a small desktop device that looks similar to a scanner a neighborhood optometrist might use for a routine eye exam. You sit on a stool, lean forward, place your chin on a padded brace, and stare into the darkness […]<\/p>\n","protected":false},"author":2,"featured_media":48198,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[49],"tags":[135,50],"class_list":["post-48197","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-microsoft-news","tag-artificial-intelligence","tag-recent-news"],"_links":{"self":[{"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/posts\/48197","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/comments?post=48197"}],"version-history":[{"count":0,"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/posts\/48197\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/media\/48198"}],"wp:attachment":[{"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/media?parent=48197"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/categories?post=48197"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sickgaming.net\/blog\/wp-json\/wp\/v2\/tags?post=48197"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}