{"id":3516,"date":"2024-05-14T15:43:57","date_gmt":"2024-05-14T19:43:57","guid":{"rendered":"https:\/\/med.virginia.edu\/diabetes-technology\/?p=3516"},"modified":"2026-06-08T11:57:13","modified_gmt":"2026-06-08T15:57:13","slug":"uva-study-adds-artificial-intelligence-software-to-artificial-pancreas","status":"publish","type":"post","link":"https:\/\/med.virginia.edu\/diabetes-technology\/2024\/05\/14\/uva-study-adds-artificial-intelligence-software-to-artificial-pancreas\/","title":{"rendered":"UVA Study Adds Artificial Intelligence Software to Artificial Pancreas"},"content":{"rendered":"

Charlottesville, Va. – By: Eric Swenson (ews3j@virginia.edu)<\/strong> Adding advanced artificial intelligence to an artificial pancreas regulating type 1 diabetes is safe and improves the system\u2019s efficiency, a first-of-its-kind<\/em> study from the University of Virginia Center for Diabetes Technology has found.<\/p>\n

In the new study, UVA researchers compared an advanced experimental artificial pancreas system \u2013 which automatically monitors and regulates blood sugar for patients with type 1 diabetes \u2013 with an artificial pancreas algorithm that incorporated AI, described by the research team as a \u201cNeural-Net Artificial Pancreas.\u201d The AI-supported artificial pancreas kept participants\u2019 blood sugar in the target range for an almost identical amount of time as the advanced system while significantly reducing computational demands, the researchers found. The increased efficiency could allow the developers to implement the system in devices with low computational power, such as an insulin pump, they say.<\/p>\n

\u201cSo far, this is the first clinical trial of a data-driven artificial pancreas system, which used a extensively trained neural network to deliver insulin automatically,” said\u00a0Boris Kovatchev, PhD, director of the UVA Center for Diabetes Technology.<\/p><\/blockquote>\n

 <\/p>\n

Enhancing the Artificial Pancreas<\/strong><\/p>\n

Fifteen adult participants with type 1 diabetes participated in back-to-back testing sessions at a hotel, using both the advanced artificial pancreas and the AI-supported artificial pancreas for 20 hours and following their daily routines as closely as possible. The AI-supported artificial pancreas system kept participants blood sugar in the target range 86% of the time, compared with 87% for the advanced artificial pancreas system. The AI-supported artificial pancreas showed greatly improved efficiency, reducing computational demands six-fold.<\/p>\n

The AI-supported artificial pancreas \u201cis therefore more suitable for implementation in devices with low processing power, such as insulin pumps or pods,\u201d the researchers report in a new scientific paper.<\/p>\n

Additional research is needed before the AI-supported artificial pancreas would be ready for use by patients. But the UVA research team hopes that incorporating AI could allow the algorithm to adapt and improve based on data from thousands of users instead of just an individual as well as to automate blood-sugar monitoring and regulation through devices with limited computing power.<\/p>\n

\u201cNeural-net implementation allows the algorithm to learn from the data of the person wearing the system,\u201d Kovatchev said. \u201cThis opens the door to real-time, AI-driven personalized insulin delivery.\u201d<\/p><\/blockquote>\n

Efforts to develop an AI-supported artificial pancreas builds on the earlier groundbreaking work of the UVA Center for Diabetes Technology to create a separate artificial pancreas system that has already been approved for use by patients. Manufactured by Tandem Diabetes Care and sold as the Control-IQ system, the U.S. Food and Drug Administration has approved the system for people ages 2 and older with type 1 diabetes.<\/p>\n

Developing innovations to improve treatment options and better patients\u2019 lives is a key mission for UVA Health \u2013 as outlined in its first-ever\u00a010-year strategic plan<\/a>\u00a0\u2013 and for UVA\u2019s forthcoming\u00a0Paul and Diane Manning Institute of Biotechnology<\/a>, now under construction at Fontaine Research Park.<\/p>\n

CBS19 News Charlottesville<\/strong><\/p>\n

Watch Video <\/a><\/p>\n

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

Charlottesville, Va. – By: Eric Swenson (ews3j@virginia.edu) Adding advanced artificial intelligence to an artificial pancreas regulating type 1 diabetes is safe and improves the system\u2019s efficiency, a first-of-its-kind study from the University of Virginia Center for Diabetes Technology has found. In the new study, UVA researchers compared an advanced experimental artificial pancreas system \u2013 which […]<\/p>\n","protected":false},"author":1866,"featured_media":3517,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[18],"tags":[22],"class_list":["post-3516","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-cdt-news-articles","tag-intranet"],"acf":false,"yoast_head":"\nUVA Study Adds Artificial Intelligence Software to Artificial Pancreas - Center for Diabetes Technology<\/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:\/\/med.virginia.edu\/diabetes-technology\/2024\/05\/14\/uva-study-adds-artificial-intelligence-software-to-artificial-pancreas\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"UVA Study Adds Artificial Intelligence Software to Artificial Pancreas - Center for Diabetes Technology\" \/>\n<meta property=\"og:description\" content=\"Charlottesville, Va. – By: Eric Swenson (ews3j@virginia.edu) Adding advanced artificial intelligence to an artificial pancreas regulating type 1 diabetes is safe and improves the system\u2019s efficiency, a first-of-its-kind study from the University of Virginia Center for Diabetes Technology has found. 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