{"id":38144,"date":"2022-09-27T14:48:40","date_gmt":"2022-09-27T13:48:40","guid":{"rendered":"https:\/\/www.he-arc.ch\/?post_type=he-arc_project&p=38144"},"modified":"2023-01-04T14:25:55","modified_gmt":"2023-01-04T13:25:55","slug":"avalglyc-io","status":"publish","type":"he-arc_project","link":"https:\/\/www.he-arc.ch\/en\/projets-recherche\/avalglyc-io\/","title":{"rendered":"AvalGlyc.io"},"content":{"rendered":"\n

Context and challenges<\/h2>\n\n\n\n

Biosensors are increasingly being developed to provide clinicians with rapid quantitative diagnostic information to guide patients\u2019 treatments without the need of a centralized laboratory testing. They can also be used in low-resource settings, without highly trained medical staff, thus having the potential to greatly improve patients\u2019 care especially regarding disease management where complex sample handling is undesirable. <\/p>\n\n\n\n

Blood glucose monitoring is a key example where technological innovation has responded to a multi-level clinical needs, from the pathology laboratory to the patient’s home. However, despite the recent progress, these methods still remain invasive and uncomfortable for the patients for daily use. <\/p>\n\n\n\n

Our approach will be a product of type point-of care and low cost with an objective to be a complementary solution to the existing invasive methods in order to reduce the number of the invasive glucose measurements.<\/p>\n\n\n\n

Objectives<\/h2>\n\n\n\n

This project\u2019s goal is to conceive and implement a non-invasive glucose monitoring system, in the form of a point-of-care system able to predict an increased risk of abnormal glycemia thus allowing patients with Type I diabetes to manage their disease in a simple and comfortable way.<\/p>\n\n\n\n

Partners and funding<\/h2>\n\n\n\n

HES-SO Valais<\/a> <\/p>\n\n\n\n

Funded by the HES-SO<\/a><\/p>\n\n\n\n

Results<\/h2>\n\n\n\n

The project has developed a flexible and extensible patient-side subsystem: <\/p>\n\n\n

\n
\"\"
Overview of the system<\/em><\/figcaption><\/figure><\/div>\n\n\n

The project is has been building a predictive mathematical model and an implementation of a system that will help to warn the patient about a potential hypo- or hyper glycemic crisis in an easy-to-manage and discreet way which will greatly impact the long-term health condition as well as the daily management of a diabetic patient:<\/p>\n\n\n

\n
\"\"
Predictive mathematical model<\/figcaption><\/figure><\/div>\n\n\n

Valorisation<\/h2>\n\n\n\n

Scientific paper – Freiburghaus, J., Rizzotti-Kaddouri, A., Albertetti, F. A Deep Learning Approach for Blood Glucose Prediction of Type 1 Diabetes.<\/p>\n\n\n\n

Project Manager<\/h2>\n\n\n\n
\n
\n
\n A\u00efcha Rizzotti\n <\/div>\n
\n +41 32 930 22 94<\/a>\n <\/div>\n
\n Aicha.Rizzotti@he-arc.ch<\/a>\n <\/div>\n <\/div>\n<\/div>\n","protected":false},"author":6,"featured_media":38145,"template":"","he-arc_domain":[3],"he-arc_project_skill":[67],"he-arc_project_year":[40,41,42,43,44],"class_list":["post-38144","he-arc_project","type-he-arc_project","status-publish","has-post-thumbnail","hentry","he-arc_domain-ingenierie","he-arc_project_skill-technologies-dinteraction","he-arc_project_year-40","he-arc_project_year-41","he-arc_project_year-42","he-arc_project_year-43","he-arc_project_year-44"],"acf":[],"yoast_head":"\nAvalGlyc.io - Haute-Ecole Arc<\/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:\/\/www.he-arc.ch\/en\/projets-recherche\/avalglyc-io\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AvalGlyc.io - Haute-Ecole Arc\" \/>\n<meta property=\"og:description\" content=\"Context and challenges Biosensors are increasingly being developed to provide clinicians with rapid quantitative diagnostic information to guide patients\u2019 treatments without the need of a centralized laboratory testing. 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