{"id":3917,"date":"2026-03-09T13:50:54","date_gmt":"2026-03-09T17:50:54","guid":{"rendered":"https:\/\/med.virginia.edu\/diabetes-technology\/?p=3917"},"modified":"2026-06-08T11:57:15","modified_gmt":"2026-06-08T15:57:15","slug":"cdt-researcher-awarded-nearly-1-million-grant-to-advance-ai-driven-diabetes-technology","status":"publish","type":"post","link":"https:\/\/med.virginia.edu\/diabetes-technology\/2026\/03\/09\/cdt-researcher-awarded-nearly-1-million-grant-to-advance-ai-driven-diabetes-technology\/","title":{"rendered":"CDT Researcher Awarded Nearly $1 Million Grant to Advance AI-Driven Diabetes Technology"},"content":{"rendered":"

March 1, 2026<\/strong> – Anas El Fathi, PhD, Assistant Professor with the University of Virginia Center for Diabetes Technology, has received a Career Development Award grant totaling $997,185<\/strong> from Breakthrough T1D to develop next-generation artificial intelligence technology designed to simplify daily management for people living with Type 1 Diabetes.<\/p>\n

\"University

Anas El Fathi, PhD<\/p><\/div>\n

The five-year award will support a research project titled \u201cABX: An AI-Driven Automated Bolus Extension to Eliminate Meal Announcements in Hybrid Closed-Loop Systems.\u201d<\/strong> The project will run from March 1, 2026 through February 28, 2031.<\/p>\n

The research team aims to develop and evaluate Automated Bolus eXtension (ABX)<\/strong>, a software upgrade for existing insulin pump systems that could automatically deliver mealtime insulin without requiring users to count carbohydrates, enter meal information, or manually deliver insulin doses. By learning from each user\u2019s glucose responses, daily habits, and insulin patterns, ABX is designed to continuously adapt and make safe insulin dosing decisions throughout the day.<\/p>\n

Even with today\u2019s advanced automated insulin delivery systems, people with type 1 diabetes must still manually estimate carbohydrates and announce meals to their insulin pump. This step can be stressful, error-prone, and is often skipped especially among teens and young adults leading to dangerous spikes in blood glucose and increased risk of long-term complications.<\/p>\n

\u201cMealtime insulin dosing remains one of the most burdensome aspects of diabetes care,\u201d said El Fathi. \u201cOur goal is to create a system that learns from each person\u2019s real-world data and safely automates this process, reducing the daily mental workload while improving glucose control.\u201d<\/p><\/blockquote>\n

The study will enroll 48 adolescents and young adults<\/strong> who use commercially available insulin pumps. Over a 20-week clinical study, researchers will evaluate how well the ABX system improves glucose outcomes and reduces the daily burden of diabetes management. Participants will compare their usual hybrid closed-loop insulin pump system with and without ABX, and later test an enhanced version of the technology that uses advanced artificial intelligence to refine insulin dosing decisions over time.<\/p>\n

Researchers will measure improvements in Time-in-Range, <\/strong>the number of hours per day blood glucose remains in a healthy range, along with reductions in glucose spikes after meals and episodes of dangerously low blood sugar.<\/p>\n

If successful, the technology could potentially be implemented as a software update to existing insulin pump systems<\/strong>, meaning patients would not need new hardware to benefit from the innovation.<\/p>\n

\u201cThis work builds on decades of research at UVA aimed at making diabetes care safer, simpler, and more automated,\u201d El Fathi said. \u201cBy removing the need for manual meal announcements, we hope to move closer to fully hands-free diabetes technology.\u201d<\/p><\/blockquote>\n

The project is supported by Breakthrough T1D through its Career Development Award program. Total grant funding may vary depending on budget adjustments and continued availability of research funds.<\/p>\n

By: Madison Maloney (mgm3t@virginia.edu)<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

March 1, 2026 – Anas El Fathi, PhD, Assistant Professor with the University of Virginia Center for Diabetes Technology, has received a Career Development Award grant totaling $997,185 from Breakthrough T1D to develop next-generation artificial intelligence technology designed to simplify daily management for people living with Type 1 Diabetes. The five-year award will support a […]<\/p>\n","protected":false},"author":1866,"featured_media":2620,"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-3917","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-cdt-news-articles","tag-intranet"],"acf":false,"yoast_head":"\nCDT Researcher Awarded Nearly $1 Million Grant to Advance AI-Driven Diabetes Technology - 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\/2026\/03\/09\/cdt-researcher-awarded-nearly-1-million-grant-to-advance-ai-driven-diabetes-technology\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"CDT Researcher Awarded Nearly $1 Million Grant to Advance AI-Driven Diabetes Technology - Center for Diabetes Technology\" \/>\n<meta property=\"og:description\" content=\"March 1, 2026 – Anas El Fathi, PhD, Assistant Professor with the University of Virginia Center for Diabetes Technology, has received a Career Development Award grant totaling $997,185 from Breakthrough T1D to develop next-generation artificial intelligence technology designed to simplify daily management for people living with Type 1 Diabetes. 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