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

Maria Panagiotou; 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

doi:10.1007/978-3-031-44240-7_8

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

Maria Panagiotou, 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^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

Abstract

People living with type 1 diabetes (PwT1D) face multiple challenges in self-managing their blood glucose levels, including the need for accurate carbohydrate counting, and the requirements of adjusting insulin dosage. Our paper aims to alleviate the demands of diabetes self-management by developing a complete system that employs computer vision to estimate the carbohydrate content of meals and utilizes reinforcement learning to personalize insulin dosing. Our findings demonstrate that this system results in a significantly greater percentage of time spent in the target glucose range compared to the combined standard bolus calculator treatment and carbohydrate counting. This approach could potentially improve glycaemic control for PwT1D and reduce the burden of carbohydrate and insulin dosage estimations.

Original language	English
Title of host publication	Computer Analysis of Images and Patterns
Subtitle of host publication	20th International Conference, CAIP 2023, Proceedings
Editors	Nicolas Tsapatsoulis, Efthyvoulos Kyriacou, Andreas Lanitis, Zenonas Theodosiou, Marios Pattichis, Constantinos Pattichis, Christos Kyrkou, Andreas Panayides
Publisher	Springer Verlag
Pages	77-86
Number of pages	10
Volume	14185 LNCS
Edition	2
ISBN (Electronic)	9783031442407
ISBN (Print)	9783031442391
DOIs	https://doi.org/10.1007/978-3-031-44240-7_8
Publication status	Published - 1 Jan 2023
Event	20th International Conference on Computer Analysis of Images and Patterns, CAIP 2023 - Limassol, Cyprus Duration: 25 Sept 2023 → 28 Sept 2023 https://cyprusconferences.org/caip2023/

Publication series

Series	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14185 LNCS
ISSN	0302-9743

Conference

Conference	20th International Conference on Computer Analysis of Images and Patterns, CAIP 2023
Abbreviated title	CAIP 2023
Country/Territory	Cyprus
City	Limassol
Period	25/09/23 → 28/09/23
Internet address	https://cyprusconferences.org/caip2023/

Keywords

Computer Vision
Deep Learning
Diabetes
Dietary Assessment
Reinforcement Learning

Access to Document

10.1007/978-3-031-44240-7_8

Cite this

Panagiotou, M., Papathanail, I., Abdur Rahman, L., Brigato, L., Bez, N. S., Vasiloglou, M. F., Stathopoulou, T., de Galan, B. E., Pedersen-Bjergaard, U., van der Horst, K., & Mougiakakou, S. (2023). A Complete AI-Based System for Dietary Assessment and Personalized Insulin Adjustment in Type 1 Diabetes Self-management. In N. Tsapatsoulis, E. Kyriacou, A. Lanitis, Z. Theodosiou, M. Pattichis, C. Pattichis, C. Kyrkou, & A. Panayides (Eds.), Computer Analysis of Images and Patterns: 20th International Conference, CAIP 2023, Proceedings (2 ed., Vol. 14185 LNCS, pp. 77-86). Springer Verlag. https://doi.org/10.1007/978-3-031-44240-7_8

Panagiotou, Maria ; Papathanail, Ioannis ; Abdur Rahman, Lubnaa et al. / A Complete AI-Based System for Dietary Assessment and Personalized Insulin Adjustment in Type 1 Diabetes Self-management. Computer Analysis of Images and Patterns: 20th International Conference, CAIP 2023, Proceedings. editor / Nicolas Tsapatsoulis ; Efthyvoulos Kyriacou ; Andreas Lanitis ; Zenonas Theodosiou ; Marios Pattichis ; Constantinos Pattichis ; Christos Kyrkou ; Andreas Panayides. Vol. 14185 LNCS 2. ed. Springer Verlag, 2023. pp. 77-86 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 14185 LNCS).

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abstract = "People living with type 1 diabetes (PwT1D) face multiple challenges in self-managing their blood glucose levels, including the need for accurate carbohydrate counting, and the requirements of adjusting insulin dosage. Our paper aims to alleviate the demands of diabetes self-management by developing a complete system that employs computer vision to estimate the carbohydrate content of meals and utilizes reinforcement learning to personalize insulin dosing. Our findings demonstrate that this system results in a significantly greater percentage of time spent in the target glucose range compared to the combined standard bolus calculator treatment and carbohydrate counting. This approach could potentially improve glycaemic control for PwT1D and reduce the burden of carbohydrate and insulin dosage estimations.",

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note = "Funding Information: This work was supported in part by the European Commission and the Swiss Confederation-State Secretariat for Education, Research and Innovation (SERI) within the project 101057730 Mobile Artificial Intelligence Solution for Diabetes Adaptive Care (MELISSA). The food images used to evaluate the system have been collected within the “Nutritional assessment: comparison between an automated tool (goFOODTM) and conventional methods” study (BASEC Number: 2020-00419). Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.; 20th International Conference on Computer Analysis of Images and Patterns, CAIP 2023, CAIP 2023 ; Conference date: 25-09-2023 Through 28-09-2023",

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Panagiotou, M, Papathanail, I, Abdur Rahman, L, Brigato, L, Bez, NS, Vasiloglou, MF, Stathopoulou, T, de Galan, BE, Pedersen-Bjergaard, U, van der Horst, K & Mougiakakou, S 2023, A Complete AI-Based System for Dietary Assessment and Personalized Insulin Adjustment in Type 1 Diabetes Self-management. in N Tsapatsoulis, E Kyriacou, A Lanitis, Z Theodosiou, M Pattichis, C Pattichis, C Kyrkou & A Panayides (eds), Computer Analysis of Images and Patterns: 20th International Conference, CAIP 2023, Proceedings. 2 edn, vol. 14185 LNCS, Springer Verlag, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14185 LNCS, pp. 77-86, 20th International Conference on Computer Analysis of Images and Patterns, CAIP 2023, Limassol, Cyprus, 25/09/23. https://doi.org/10.1007/978-3-031-44240-7_8

A Complete AI-Based System for Dietary Assessment and Personalized Insulin Adjustment in Type 1 Diabetes Self-management. / Panagiotou, Maria; Papathanail, Ioannis; Abdur Rahman, Lubnaa et al.
Computer Analysis of Images and Patterns: 20th International Conference, CAIP 2023, Proceedings. ed. / Nicolas Tsapatsoulis; Efthyvoulos Kyriacou; Andreas Lanitis; Zenonas Theodosiou; Marios Pattichis; Constantinos Pattichis; Christos Kyrkou; Andreas Panayides. Vol. 14185 LNCS 2. ed. Springer Verlag, 2023. p. 77-86 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 14185 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

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AU - Bez, Natalie S.

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AU - van der Horst, Klazine

AU - Mougiakakou, Stavroula

N1 - Funding Information: This work was supported in part by the European Commission and the Swiss Confederation-State Secretariat for Education, Research and Innovation (SERI) within the project 101057730 Mobile Artificial Intelligence Solution for Diabetes Adaptive Care (MELISSA). The food images used to evaluate the system have been collected within the “Nutritional assessment: comparison between an automated tool (goFOODTM) and conventional methods” study (BASEC Number: 2020-00419). Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

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N2 - People living with type 1 diabetes (PwT1D) face multiple challenges in self-managing their blood glucose levels, including the need for accurate carbohydrate counting, and the requirements of adjusting insulin dosage. Our paper aims to alleviate the demands of diabetes self-management by developing a complete system that employs computer vision to estimate the carbohydrate content of meals and utilizes reinforcement learning to personalize insulin dosing. Our findings demonstrate that this system results in a significantly greater percentage of time spent in the target glucose range compared to the combined standard bolus calculator treatment and carbohydrate counting. This approach could potentially improve glycaemic control for PwT1D and reduce the burden of carbohydrate and insulin dosage estimations.

AB - People living with type 1 diabetes (PwT1D) face multiple challenges in self-managing their blood glucose levels, including the need for accurate carbohydrate counting, and the requirements of adjusting insulin dosage. Our paper aims to alleviate the demands of diabetes self-management by developing a complete system that employs computer vision to estimate the carbohydrate content of meals and utilizes reinforcement learning to personalize insulin dosing. Our findings demonstrate that this system results in a significantly greater percentage of time spent in the target glucose range compared to the combined standard bolus calculator treatment and carbohydrate counting. This approach could potentially improve glycaemic control for PwT1D and reduce the burden of carbohydrate and insulin dosage estimations.

KW - Computer Vision

KW - Deep Learning

KW - Diabetes

KW - Dietary Assessment

KW - Reinforcement Learning

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Computer Analysis of Images and Patterns

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A2 - Lanitis, Andreas

A2 - Theodosiou, Zenonas

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A2 - Pattichis, Constantinos

A2 - Kyrkou, Christos

A2 - Panayides, Andreas

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Panagiotou M, Papathanail I, Abdur Rahman L, Brigato L, Bez NS, Vasiloglou MF et al. A Complete AI-Based System for Dietary Assessment and Personalized Insulin Adjustment in Type 1 Diabetes Self-management. In Tsapatsoulis N, Kyriacou E, Lanitis A, Theodosiou Z, Pattichis M, Pattichis C, Kyrkou C, Panayides A, editors, Computer Analysis of Images and Patterns: 20th International Conference, CAIP 2023, Proceedings. 2 ed. Vol. 14185 LNCS. Springer Verlag. 2023. p. 77-86. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 14185 LNCS). doi: 10.1007/978-3-031-44240-7_8