{"id":2929,"date":"2023-09-25T09:35:38","date_gmt":"2023-09-25T08:35:38","guid":{"rendered":"https:\/\/springerhealthcare.nl\/france\/?p=2929"},"modified":"2023-09-29T09:59:05","modified_gmt":"2023-09-29T08:59:05","slug":"daily-briefing-ai-detects-risk-of-parkinsons-from-retinal-images","status":"publish","type":"post","link":"https:\/\/springerhealthplus.nl\/shmigrate\/daily-briefing-ai-detects-risk-of-parkinsons-from-retinal-images\/","title":{"rendered":"AI detects risk of Parkinson\u2019s from retinal images"},"content":{"rendered":"

[vc_row full_width=”stretch_row” content_placement=”middle” css=”.vc_custom_1663187695627{padding-bottom: 37px !important;background-image: url(http:\/\/springerhealthcare.nl\/wp-content\/uploads\/sites\/24\/2022\/09\/science_connect_back.png?id=2486) !important;background-position: center !important;background-repeat: no-repeat !important;background-size: cover !important;}” el_class=”sciencetitle”][vc_column][vc_custom_heading text=”Science Connect” font_container=”tag:h2|text_align:left|color:%23ffffff” use_theme_fonts=”yes” link=”url:https%3A%2F%2Fspringerhealthcare.nl%2Ffrance%2Fscience-connect-september-2023%2F”][vc_column_text css=”.vc_custom_1663186747780{margin-bottom: 40px !important;}”]A Springer Healthcare Initiative For Pharma Professionals<\/span>[\/vc_column_text][vc_column_text]SEPTEMBER 2023
\n<\/span>[\/vc_column_text][\/vc_column][\/vc_row][vc_row css=”.vc_custom_1538651642953{padding-top: 75px !important;}”][vc_column width=”3\/4″ css=”.vc_custom_1538657061395{border-right-width: 1px !important;padding-top: 0px !important;border-right-color: #8faec1 !important;border-right-style: solid !important;}”][vc_custom_heading source=”post_title” font_container=”tag:h1|font_size:40px|text_align:left|line_height:60px” google_fonts=”font_family:Merriweather%3Aregular%2Citalic|font_style:700%20regular%3A700%3Anormal” el_class=”title” css=”.vc_custom_1664358136969{margin-bottom: 30px !important;}”][vc_column_text css=”.vc_custom_1686833173052{padding-bottom: 0px !important;}”]Researchers have developed a model trained similarly to ChatGPT that can be adapted to evaluate multiple health conditions.<\/strong><\/span>[\/vc_column_text][vc_column_text el_class=”intro-box”]By Mariana Lenharo<\/span>[\/vc_column_text][vc_single_image image=”2944″ img_size=”full” el_class=”hero-img”][vc_column_text css=”.vc_custom_1695903828178{margin-bottom: 30px !important;}” el_class=”sc-img-caption”]Retinal imaging allows researchers and physicians to observe small blood vessels whose condition could hint at a health-care issue. Credit: ipm\/Alamy[\/vc_column_text][vc_column_text]

Scientists have developed an artificial intelligence (AI) tool capable of diagnosing and predicting the risk of developing multiple health conditions \u2014 from ocular diseases to heart failure to Parkinson\u2019s disease \u2014 all on the basis of people\u2019s retinal images.<\/p>\n

AI tools have been trained to detect disease using retinal images before, but what makes the new tool \u2014 called RETFound \u2014 special is that it was developed using a method known as self-supervised learning. That means that the researchers did not have to analyse each of the 1.6 million retinal images used for training and label them as \u2018normal\u2019 or \u2018not normal\u2019, for instance. Such procedures are time-consuming and expensive, and are needed during the development of most standard machine-learning models.<\/p>\n

Instead, the scientists used a method similar to the one used to train large language models such as ChatGPT. That AI tool harnesses myriad examples of human-generated text to learn how to predict the next word in a sentence from the context of the preceding words. In the same kind of way, RETFound uses a multitude of retinal photos to learn how to predict what missing portions of images should look like.<\/p>\n

\u201cOver the course of millions of images, the model somehow learns what a retina looks like and what all the features of a retina are,\u201d says Pearse Keane, an ophthalmologist at Moorfields Eye Hospital NHS Foundation Trust in London who co-authored a paper published today in\u00a0Nature<\/em>1<\/a><\/sup>\u00a0describing the tool. This forms the cornerstone of the model, and classifies it as what some call a foundation model, which means that it can be adapted for many tasks.<\/p>\n

A window into human health<\/h2>\n

A person\u2019s retinas can offer a window into their health, because they are the only part of the human body through which the capillary network, made up of the smallest blood vessels, can be observed directly. \u201cIf you have some systemic cardiovascular disease, like hypertension, which is affecting potentially every blood vessel in your body, we can directly visualize [that] in retinal images,\u201d Keane says.<\/p>\n

Retinas are also an extension of the central nervous system, sharing similarities with the brain, which means that retinal images can be used to evaluate neural tissue. \u201cThe rub is that a lot of the time people don\u2019t have the expertise to interpret these scans. This is where AI comes in,\u201d Keane says.<\/p>\n

Once they had pre-trained RETFound on those 1.6 million unlabelled retinal images, Keane and his colleagues could then introduce a small number of labelled images \u2014 say, 100 retinal images from people who had developed Parkinson\u2019s and 100 from people who had not \u2014 to teach the model about specific conditions. Having learnt from all the unlabelled images what a retina should look like, Keane says, the model is able to easily learn the retinal features associated with a disease.<\/p>\n

Using unlabelled data to initially train the model \u201cunblocks a major bottleneck for researchers\u201d, says Xiaoxuan Liu, a clinical researcher who studies responsible innovation in AI at the University of Birmingham, UK. Radiologist Curtis Langlotz, director of the Center for Artificial Intelligence in Medicine and Imaging at Stanford University in California, agrees. \u201cHigh-quality labels for medical data are extremely expensive, so label efficiency has become the coin of the realm,\u201d he says.<\/p>\n

The system performed well at detecting ocular diseases such as diabetic retinopathy. On a scale where 0.5 represents a model that performs no better than a random prediction and 1 represents a perfect model that makes an accurate prediction each time, it scored between 0.822 and 0.943 for diabetic retinopathy, depending on the data set used. When predicting the risk for systemic diseases \u2014 such as heart attacks, heart failure, stroke and Parkinson\u2019s \u2014 the overall performance was limited, but still superior to that of other AI models.<\/p>\n

RETFound is so far one of the few successful applications of a foundation model to medical imaging, Liu says.<\/p>\n

Expanding applications<\/h2>\n

Researchers are now looking ahead to what other types of medical imaging the techniques used to develop RETFound might be applied to. \u201cIt will be interesting to see whether these methods generalize to more complex images,\u201d Langlotz says \u2014 for example, to magnetic resonance images or computed tomography scans, which are often three- or even four-dimensional.<\/p>\n

The authors have made the model publicly available, and hope that groups around the world will be able to adapt and train it to work for their own patient populations and medical settings. \u201cThey could potentially take this algorithm and fine-tune it, using data from their own country to have something that\u2019s more optimized for their use,\u201d Keane says.<\/p>\n

\u201cThis is tremendously exciting,\u201d Liu says. But using RETFound as the basis for other models to detect diseases comes with a risk, she adds. That\u2019s because any limitations embedded in the tool could leak into future models that are built from it. \u201cIt is now up to the authors of RETFound to ensure its ethical and safe usage, including transparent communication of its limitations, so that it can be a true community asset.\u201d<\/p>\n

doi: https:\/\/doi.org\/10.1038\/d41586-023-02881-2<\/em><\/p>\n

References<\/h2>\n
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    1. Zhou, Y. et al. Nature<\/em> https:\/\/doi.org\/10.1038\/s41586-023-06555-x<\/a> (2023).<\/li>\n<\/ol>\n<\/li>\n<\/ol>\n[\/vc_column_text][vc_column_text]\u00a9 2023 Springer Nature Limited.[\/vc_column_text][\/vc_column][vc_column width=”1\/4″ css=”.vc_custom_1538657003956{padding-top: 0px !important;padding-bottom: 0px !important;}”][vc_custom_heading text=”ISSUES” font_container=”tag:h3|font_size:12px|text_align:left|color:%23333333|line_height:18px” use_theme_fonts=”yes” el_class=”titlewidget”][vc_column_text el_class=”columnflex” css=”.vc_custom_1664404002021{margin-bottom: 40px !important;}”]

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