Mobile Artificial Intelligence Solution for Diabetes Adaptive Care

Informazioni relative al progetto

MELISSA

ID dell’accordo di sovvenzione: 101057730

DOI

10.3030/101057730

Data della firma CE 23 Maggio 2022

Data di avvio 1 Giugno 2022

Data di completamento 31 Maggio 2027

Finanziato da

• Health

Costo totale

€ 6 061 709,00

Contributo UE

€ 5 986 709,00

Coordinato da

UNIVERSITEIT MAASTRICHT
Netherlands

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

Documents, reports (7)

Questionnaires for user requirements

Questionnaires for user: The questionnaires will consist of questions related to diabetes management and treatment as well as the user’s familiarity with diabetes-related apps.

Standard Operational Procedures

Standard Operational Procedures of the clinical studies of WP4

Audio-visual material

Audio-visual material will include an animated video to explain key features and results of the project.

End-user survey

End-user survey: Products/apps which already exist in the market related to diabetes will be reviewed and analyzed regarding their pros and cons and the benefit of the new app approach of the MELISSA project will be compared

First study subject approvals package
Ethical framework for a trustworthy implementation of ABBA

Recommendations for the trustworthy implementation of AI-driven diabetes management apps, addressing ethical obstacles and proposing insights to mitigate them.

Final recruitment plan

Final plan on recruiting of patients to the clinical studies of WP4

Websites, patent fillings, videos etc. (2)

Public project website

The website will inform the public about the projects aims updates and results The website will be continuously updated throughout the projects lifetime Links to social media will be implemented as well

Results for ABBA's in-silico validation

Results of the in-silico simulations achieved by the algorithm to be integrated into MELISSA's mobile application

Pubblicazioni

Peer reviewed articles (3)

The effect of bolus advisors on glycaemic parameters in adults with diabetes on intensive insulin therapy: A systematic review with meta‐analysis

Autori: Elisabeth J. den Brok; Cecilie H. Svensson; Maria Panagiotou; Marleen M. J. van Greevenbroek; Peter R. Mertens; Andriani Vazeou; Asimina Mitrakou; Konstantinos Makrilakis; Gregor H. L. M. Franssen; Sa
Pubblicato in: Diabetes, Obesity and Metabolism, 2024, ISSN 1463-1326
Editore: Wiley
DOI: 10.48350/194556

Nutrients

Autori: Ioannis Papathanail; Lubnaa Abdur Rahman; Lorenzo Brigato; Natalie Bez; Maria Vasiloglou; Klazine van der Horst; Stavroula Mougiakakou
Pubblicato in: Nutrients, 2023, ISSN 2072-6643
Editore: MDPI
DOI: 10.48350/186190

Role of artificial intelligence in enhancing insulin recommendations and therapy outcomes

Autori: Panagiotou, Maria; Strommen, Knut; Brigato, Lorenzo; de Galan, Bastiaan E.; Mougiakakou, Stavroula
Pubblicato in: Die Diabetologie, 2025, ISSN 2731-7447
Editore: Springer Nature
DOI: 10.48550/ARXIV.2503.18592

Conference proceedings (3)

A Comparative Analysis of Sensor-, Geometry-, and Neural-Based Methods for Food Volume Estimation

Autori: Lubnaa Abdur Rahman, Ioannis Papathanail, Lorenzo Brigato, Stavroula Mougiakakou
Pubblicato in: Proceedings of the 8th International Workshop on Multimedia Assisted Dietary Management, 2023, ISBN 979-8-4007-0284-6
Editore: ACM
DOI: 10.1145/3607828.3617794

A Complete AI-Based System for Dietary Assessment and Personalized Insulin Adjustment in Type 1 Diabetes Self-management

Autori: Panagiotou, Maria, Ioannis Papathanail, Lubnaa Abdur Rahman, Lorenzo Brigato, Natalie S. Bez, Maria F. Vasiloglou, Thomai Stathopoulou, Bastiaan E. de Galan, Ulrik Pedersen-Bjergaard, Klazine van der Horst, Stavroula Mougiakakou
Pubblicato in: International Conference on Computer Analysis of Images and Patterns, 2023, ISBN 978-3-031-44240-7
Editore: Springer, Cham
DOI: 10.1007/978-3-031-44240-7_8

A SAM Based Tool for Semi-Automatic Food Annotation

Autori: Rahman, Lubnaa Abdur; Papathanail, Ioannis; Brigato, Lorenzo; Mougiakakou, Stavroula
Pubblicato in: Frontiers in Artificial Intelligence and Applications ISBN: 9781643685489, 2024
Editore: IOS Press
DOI: 10.48550/ARXIV.2410.19756

Book chapters (2)

Benchmarking Post-Hoc Unknown-Category Detection in Food Recognition

Autori: Lubnaa Abdur Rahman, Ioannis Papathanail, Lorenzo Brigato, Stavroula Mougiakakou
Pubblicato in: Lecture Notes in Computer Science, Pattern Recognition. ICPR 2024 International Workshops and Challenges, 2025
Editore: Springer Nature Switzerland
DOI: 10.48550/ARXIV.2503.18548

Food Recognition and Nutritional Apps

Autori: Rahman, Lubnaa Abdur; Papathanail, Ioannis; Brigato, Lorenzo; Spanakis, Elias K.; Mougiakakou, Stavroula
Pubblicato in: Diabetes Digital Health, Telehealth, and Artificial Intelligence, 2023, ISBN 9780323906760
Editore: Elsevier