{"id":31178,"date":"2022-12-17T06:00:00","date_gmt":"2022-12-17T14:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=31178"},"modified":"2022-12-15T18:07:53","modified_gmt":"2022-12-16T02:07:53","slug":"how-ai-can-be-used-to-help-people-see","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2022\/12\/17\/how-ai-can-be-used-to-help-people-see\/","title":{"rendered":"How AI Can be Used to Help People See"},"content":{"rendered":"
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In this special guest feature,\u00a0Dr George Magrath, CEO of Lexitas<\/a>, explains how AI is being harnessed in the development of eye care medicines. Lexitas is a 160-person company which partners with pharmaceutical firms to develop novel eye care drugs by running clinical trials. Dr Magrath is a trained ophthalmologist who, despite his busy day-to-day work leading Lexitas, still takes one day out every week to maintain his practice and treat patients with rare eye conditions. In fact, he is the only physician in his home state of South Carolina with fellowship training in treating cancers in and around the eye.<\/em><\/p>\n\n\n\n

The potential of AI to make people healthier is revolutionary.<\/p>\n\n\n\n

I\u2019ve seen that potential first hand as we develop new medicines. I run a company which partners with pharmaceutical firms to develop novel eye care drugs by running clinical trials, and AI is playing an increasing role in these trials.<\/p>\n\n\n\n

Put simply, it is already becoming a genuine force for making treatments better.<\/p>\n\n\n\n

AI is transforming the development of new eye care drugs in ophthalmic clinical trials in two key ways. The first is to help pick out patients who are most likely to benefit from a potential treatment.<\/p>\n\n\n\n

AI is being used to identify these patients based on imaging of their eyes before inclusion in a trial. A good example of this relates to age-related macular degeneration (AMD), a condition which can cause some patients to eventually suffer severe vision loss, while others do not.<\/p>\n\n\n\n

By using AI and deep learning, you can train a machine to analyze historical imaging of patients who progressed with AMD and those who did not progress. Once the machine has learned to pick out which patients might or might not progress to AMD, you can then apply it to images of future <\/em>patients who you\u2019re considering putting into a clinical trial.<\/p>\n\n\n\n

This means the AI can then predict the patients most likely to progress to severe vision loss – resulting in them being chosen for enrollment on a trial as they are most likely to benefit from participating. Being able to pick out the most suited patients, with less trial and error, is an extremely powerful tool because it improves data outputs and also cuts out unnecessary exposure to risk and potential side effects among those people who are unlikely to need the treatment anyway.<\/p>\n\n\n\n

Second, AI is completely changing the way we can evaluate drug effectiveness data coming out of clinical trials. The data allow us to learn more about how our medicines work as we test them. <\/p>\n\n\n\n

One recent example of this that I oversaw was in a trial of a potential treatment for diabetic eye disease, the leading cause of blindness in working age adults in the US. We used AI to identify and quantify the volume of fluid in the retina over time on a 3D reconstruction using optical coherence tomography (OCT). The AI was able to map the fluid and quantify the change in fluid volume – i.e. swelling – in different areas of the retina, giving us amazing insight into the effect of the investigational product on the patients.<\/p>\n\n\n\n

This is a specific example of evaluating the effect of a drug in ways we could not without AI, as it\u2019s impossible for the naked human eye to pick up. Self-evidently, the purpose of a clinical trial is to learn as much as you can about a drug and its biological effect, so this is hugely important progress.<\/p>\n\n\n\n

So, through predicting patients most at risk of sight loss, and providing ultra-precise data giving unprecedented insight into the biological effect of treatments, AI is already a powerful force in the ophthalmic clinical trial space – with so much further potential going forward. And the more this can be harnessed, the more it will help people see.<\/p>\n\n\n\n

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From reducing the reliance on trial and error to providing ultra-precise drug effectiveness data, AI has a major role to play in making clinical trials more efficient and robust. In this contributed article, ophthalmologist and biopharma CEO Dr George Magrath explains how AI is being harnessed in the development of eye care medicines.<\/p>\n","protected":false},"author":37,"featured_media":31179,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[526,115,87,180,74,61,122,67,56,97,1],"tags":[],"acf":[],"yoast_head":"\nHow AI Can be Used to Help People See - insideBIGDATA<\/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:\/\/insidebigdata.com\/2022\/12\/17\/how-ai-can-be-used-to-help-people-see\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How AI Can be Used to Help People See - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"From reducing the reliance on trial and error to providing ultra-precise drug effectiveness data, AI has a major role to play in making clinical trials more efficient and robust. 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