{"id":1783,"date":"2022-04-07T19:55:53","date_gmt":"2022-04-07T19:55:53","guid":{"rendered":"https:\/\/healthtech.berkeley.edu\/?page_id=1783"},"modified":"2022-04-07T19:56:50","modified_gmt":"2022-04-07T19:56:50","slug":"retinopathy","status":"publish","type":"page","link":"https:\/\/diagnostic.berkeley.edu\/index.php\/retinopathy\/","title":{"rendered":"Retinopathy"},"content":{"rendered":"\t\t
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Diabetic Retinopathy Detection with Machine Learning<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Abigail Woolf was reading a research paper in her AI for Healthcare class about the success of a convolutional neural network \u2014 artificial neurons used to analyze visual imagery \u2014 that could detect referable diabetic retinopathy, a preventable but major cause of blindness around the world. The paper impressed her, but it was mum on actually utilizing an algorithm with so much potential in clinical settings. \u201cI asked in class why the technology hadn\u2019t been deployed,\u201d says the Berkeley master of development engineering student, \u201cand the professor said that it was complicated to standardize the data and processes behind everything.\u201d<\/span><\/p>

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Her aunt, who has diabetes, has to make frequent treks to the doctor\u2019s office to get her eyes checked. Woolf also knew there were cheap lenses that could be attached to iPhones for use in clinical settings. What if she could combine these powerful algorithms for detecting diabetic retinopathy \u2014 which can be more accurate than doctors \u2014 with these lenses that diabetics could use at home? It would save folks like her aunt time and money, while allowing ophthalmologists to spend more time on treating cases and less on diagnostics. Woolf, a 2022 <\/span>Big Ideas<\/span><\/a> Contest finalist, envisions \u201ca data\/camera package that can be sold or donated as a single unit to clinics for automated DR diagnostics.\u201d<\/span><\/p>

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Through Big Ideas and the CoLab, Woolf will continue her research and look for partners for her diagnostic innovation. She\u2019s reaching out to some of the researchers who worked on that paper from her AI for Healthcare class and asking them why the algorithm \u201chasn\u2019t been deployed in the wild.\u201d<\/span><\/p>

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\u201cI am waiting for someone to tell me, \u2018this idea is impossible because\u2026\u2019\u201d Abigail says. \u201cBut until then, I will believe that it is possible.\u201d<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Diabetic Retinopathy Detection with Machine Learning Abigail Woolf was reading a research paper in her AI for Healthcare class about the success of a convolutional neural network \u2014 artificial neurons used to analyze visual imagery \u2014 that could detect referable diabetic retinopathy, a preventable but major cause of blindness around the world. The paper impressed […]<\/p>\n","protected":false},"author":6,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"elementor_header_footer","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-1783","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"\nRetinopathy - UC Berkeley Health Tech CoLab<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/healthtech.berkeley.edu\/index.php\/retinopathy\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Retinopathy - UC Berkeley Health Tech CoLab\" \/>\n<meta property=\"og:description\" content=\"Diabetic Retinopathy Detection with Machine Learning Abigail Woolf was reading a research paper in her AI for Healthcare class about the success of a convolutional neural network \u2014 artificial neurons used to analyze visual imagery \u2014 that could detect referable diabetic retinopathy, a preventable but major cause of blindness around the world. 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