Publications
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2025

E.J. den Brok, C. Hornborg‐Svensson, N. M. Dimitropoulou, S. Broeng‐Mikkelgaard, M. T. Olsen, L. Abdur Rahman, I. Papathanail, A. Wiede, J. Peters, P. R. Mertens, E. Zikou

Study protocol for exploring diabetes numeracy and health literacy across Europe (EDUCATE): A multicentre cross‐sectional study. Diabetic Medicine, 42(11), p.e70140.

L. Abdur Rahman, I. Papathanail, S. Mougiakakou

Introducing the Swiss Food Knowledge Graph: AI for Context-Aware Nutrition Recommendation. MMFood’25, p.27.doi: https://doi.org/10.1145/3746264.3760494

S. Zumthurm, I. Papathanail, L. Abdur Rahman, L. Brigato, S. Mougiakakou, A. Stämpfli

Reducing meat consumption using a diet-related written prompt and the Swiss food pyramid: A field study. Food Quality and Preference, 126, p.105416. doi: https://doi.org/10.1016/j.foodqual.2024.105416

2024

L. Abdur Rahman, I. Papathanail, L. Brigato, S. Mougiakakou

Benchmarking Post-Hoc Unknown-Category Detection in Food Recognition. International Conference on Pattern Recognition (pp. 3-18). Cham: Springer Nature Switzerland.doi: https://doi.org/10.1007/978-3-031-88217-3_1

L. Abdur Rahman, I. Papathanail, L. Brigato, S. Mougiakakou

A SAM based tool for semi-automatic food annotation. rontiers in Artificial Intelligence and Applications, vol. 392, pp. 4475–4478. doi:
https://doi.org/10.3233/FAIA24103

L. Abdur Rahman, I. Papathanail, L. Brigato, E. K. Spanakis, S. Mougiakakou

Food recognition and nutritional apps. Diabetes Digital Health, Telehealth, and Artificial Intelligence (pp. 73-83). Academic Press. doi: https://doi.org/10.1016/B978-0-443-13244-5.00015-8

2023

L. Abdur Rahman, I. Papathanail, L. Brigato, S. Mougiakakou

A Comparative Analysis of Sensor-, Geometry-, and Neural-Based Methods for Food Volume Estimation doi: https://doi.org/10.1145/3607828.3617794

M. Panagiotou, I. Papathanail, L. Abdur Rahman, L. Brigato, N. Bez, M. Vasiloglou, ... & S. Mougiakakou

A Complete AI-Based System for Dietary Assessment and Personalized Insulin Adjustment in Type 1 Diabetes Self-management doi: https://doi.org/10.1007/978-3-031-44240-7_8

I. Papathanail, L. Abdur Rahman, L. Brigato, N. Bez, M. Vasiloglou, K. van der Horst, & S. Mougiakakou

The Nutritional Content of Meal Images in Free-Living Conditions—Automatic Assessment with goFOODTM doi: https://doi.org/10.3390/nu15173835

2022

I. Papathanail, M. F. Vasiloglou, T. Stathopoulou, A. Ghosh, M. Baumann, D. Faeh, S. Mougiakakou

A feasibility study to assess Mediterranean Diet adherence using an AI-powered system. Scientific reports, 12(1), p. 17008. Springer Nature. doi: 10.1038/s41598-022-21421-y

M. F. Vasiloglou, I. Marcano, S. Lizama, I. Papathanail, E. K. Spanakis, S. Mougiakakou

Multimedia Data-Based Mobile Applications for Dietary Assessment Journal of diabetes science and technology, p. 19322968221085026. Sage. doi: 10.1177/19322968221085026

2021

I. Papathanail, J. Brühlmann, M. F. Vasiloglou, T. Stathopoulou, A. K. Exadaktylos, Z. Stanga, T. Münzer, S. Mougiakakou

Evaluation of a Novel Artificial Intelligence System to Monitor and Assess Energy and Macronutrient Intake in Hospitalised Older Patients Nutrients, 13(12) Molecular Diversity Preservation International MDPI. doi: 10.3390/nu13124539

I. Papathanail, Y. Lu, A. Ghosh, S. Mougiakakou

Food Recognition in the Presence of Label Noise. International Conference on Pattern Recognition (MADiMa ’20), 2021, pp. 617-628. doi: 10.1007/978-3-030-68821-9_49

I. Papathanail, Y. Lu, M. F. Vasiloglou, T. Stathopoulou, A. Ghosh, D. Faeh, S. Mougiakakou

Food Recognition in Assessing the Mediterranean Diet: A Hierarchical Approach. Advanced Technologies & Treatments for Diabetes (ATTD) 2021, P248/#602 doi: 10.1089/dia.2021.2525.abstracts

M.F. Vasiloglou, S. Christodoulidis, E. Reber, T. Stathopoulou, Y. Lu, Z. Stanga, S. Mougiakakou

Perspectives and Preferences of Adult Smartphone Users Regarding Nutrition and Diet Apps: Web-Based Survey Study JMIR mHealth and uHealth 2021; 9(7):e27885. doi: 10.2196/27885

E. Reber, K. A. Schönenberger, M.F. Vasiloglou, Z. Stanga

Nutritional Risk Screening in Cancer Patients: The First Step Toward Better Clinical Outcome Frontiers in Nutrition 2021. doi: 10.3389/fnut.2021.603936

S. Kurmann, E. Reber, M.F. Vasiloglou, P. Schuetz, A.W. Schoenenberger, K.Uhlmann, A.B Sterchi, Z. Stanga

Energy and protein intake in medical and geriatric inpatients with MEDPass versus conventional administration of oral nutritional supplements: study protocol for the randomized controlled MEDPass Trial Trials 2021; 22, 210. doi: 10.1186/s13063-021-05145-4

M.F. Vasiloglou, K. van der Horst, T. Stathopoulou, M.P. Jaeggi, G.S. Tedde, Y. Lu, S. Mougiakakou

The Human Factor in Automated Image-Based Nutrition Apps: Analysis of Common Mistakes Using the goFOOD Lite App. JMIR mHealth and uHealth 2021; 9(1):e24467. doi: 10.2196/24467

Y. Lu, T. Stathopoulou, S. Mougiakakou

Partially Supervised Multi-Task Network for Single-View Dietary Assessment ICPR2020. Milano (Virtual). Jan 10-15, 2021. doi: 2008.00818

2020

M.F. Vasiloglou, Y. Lu, T. Stathopoulou, I. Papathanail, D. Faeh, A. Ghosh, M. Baumann, S. Mougiakakou

Assessing Mediterranean Diet Adherence with the Smartphone: The Medipiatto Project.. Nutrients 2020, 12, 3763. doi: 10.3390/nu12123763

Y. Lu, T. Stathopoulou, M.F. Vasiloglou, L.F. Pinault, C. Kiley, E.K. Spanakis, S. Mougiakakou

goFOODTM: An Artificial Intelligence System for Dietary Assessment.. Sensors 2020, 20, 4283. doi: 10.3390/s20154283

M.F. Vasiloglou, S. Christodoulidis, E. Reber, T. Stathopoulou, Y. Lu, Z. Stanga, S. Mougiakakou

What Healthcare Professionals Think of “Nutrition & Diet” Apps: An International Survey. . Nutrients 2020, 12, 2214. doi: 10.3390/nu12082214

Y. Lu, T. Stathopoulou, M. F. Vasiloglou, S. Christodoulidis, Z. Stanga and S. Mougiakakou

An Artificial Intelligence-Based System to Assess Nutrient Intake for Hospitalised Patients. IEEE Transactions on Multimedia. doi: 10.1109/TMM.2020.2993948 , May, 2020.

M. F. Vasiloglou, Z. Stanga, S. Mougiakakou

Nutrition and diet” apps: A preliminary analysis of a survey between medical doctors and nurses[#689], 13th International Conference on Advanced Technologies & Treatments for Diabetes (ATTD2020), Madid, Spain, February 2020.

2019

E. Reber, N. Friedli, M.F. Vasiloglou, P. Schuetz, Z. Stanga

Management of Refeeding Syndrome in Medical Inpatients. Journal of Clinical Medicine. 8(12): 2202, December, 2019.

M.F. Vasiloglou, Y. Lu, T. Stathopoulou, L.F. Pinault, S. Christodoulidis, L.G. Singh, G.S. Tedde, M.P. Jäggi, E. Spanakis, S. Mougiakakou

A comparison of macronutrient and energy content estimates by goFOOD vs dietitians: A Preliminary Analysis. In 19th Annual Diabetes Technology Meeting, Bethesda, Maryland, November, 2019 (GOLD Student Award).

E. Reber, F. Gomes, I.A. Dähn, M.F. Vasiloglou, Z. Stanga

Management of Dehydration in Patients Suffering Swallowing Difficulties. Journal of Clinical Medicine. 8(11): E1923, November, 2019.

M. Fontanellaz, S. Christodoulidis, S. Mougiakakou

Self-Attention and Ingredient-Attention Based Model for Recipe Retrieval from Image Queries. In Proceedings of the 5th International Workshop on Multimedia Assisted Dietary Management (MADiMa ’19). ACM, New York, NY, USA, 25-31. DOI: https://doi.org/10.1145/3347448.3357163. (Oral presentation)

M.F. Vasiloglou, J. Fletcher, K. A. Poulia

Challenges and Perspectives in Nutritional Counselling and Nursing: A Narrative Review. Journal of Clinical Medicine. 8(9):1489, September, 2019.

E. Reber, F. Gomes, M.F. Vasiloglou, P. Schuetz, Z. Stanga

Nutritional Risk Screening and Assessment. Journal of Clinical Medicine, 8(7): 1065, July 2019.

C. Cuerda, M.F. Vasiloglou, L. Arhip

Nutritional Management and Outcomes in Malnourished Medical Inpatients: Anorexia Nervosa. Journal of Clinical Medicine, 2019, 8(7), 1042.

Y. Lu, T. Stathopoulou, M. F. Vasiloglou, S. Christodoulidis, B. Blum, T. Walser, V. Meier, Z. Stanga, S. Mougiakakou

An Artificial Intelligence-Based System for Nutrient Intake Assessment of Hospitalised Patients, 41st IEEE Engineering in Medicine and Biology Conference (IEEE EMBC2019), Berlin, Germany, July 2019 (Oral presentation).

Y. Lu, M. F. Vasiloglou, T. Stathopoulou, S. Christodoulidis, S. Mougiakakou

An Enhanced AI and Computer Vision-based Smartphone System for Translating Food Multimedia Data to Nutrient Information, 12th International Conference on Advanced Technologies & Treatments for Diabetes (ATTD2019), Berlin, Germany, February 2019.

M. F. Vasiloglou, Y. Lu, T. Stathopoulou, S. Christodoulidis, Z. Stanga , S. Mougiakakou

Automatic Nutrient Estimation for Hospitalised Patients[#105]. 12th International Conference on Advanced Technologies & Treatments for Diabetes (ATTD2019), Berlin, Germany, February 2019.

2018

Y. Lu, D. Allegra, M. Anthimopoulos, F. Stanco, G.M. Farinella, S. Mougiakakou

A Multi-Task Learning Approach for Meal Assessment. In Proceedings of the Joint Workshop on Multimedia for Cooking and Eating Activities and Multimedia Assisted Dietary Management (CEA/MADiMa ’18). ACM, New York, NY, USA, 46-52. https://doi.org/10.1145/3230519.3230593. (Best Student Student Travel Grant).

M. F. Vasiloglou, S. Mougiakakou, E. Aubry, A. Bokelmann, R. Fricker, F. Gomes, C. Guntermann, A. Meyer, D. Studerus, Z. Stanga

A Comparative Study on Carbohydrate Estimation: GoCARB vs. Dietitians. Nutrients, 2018, 10, 741. 10.3390/nu10060741

M. Vasiloglou, Z. Stanga, S. Mougiakakou

GoCARB Accuracy On Carbohydrate Estimation Versus Visual Estimations By Dietitians, 11th International Conference on Advanced Technologies and Treatments for Diabetes (ATTD2018), Vienna, Austria, February 2018.

2017

J. Dehais, M. Anthimopoulos, S. Shevchik, S. Mougiakakou

Two-view 3D Reconstruction for Food Volume Estimation. IEEE Transactions on Multimedia, 19(5): 1090-1099, 2017. 10.1109/TMM.2016.2642792

D. Allegra, M. Anthimopoulos, J. Dehais, Y. Lu, F. Stanco, G.M. Farinella, S. Mougiakakou

A Multimedia Database for Automatic Meal Assessment Systems. In: Battiato S., Farinella G., Leo M., Gallo G. (eds) New Trends in Image Analysis and Processing – ICIAP 2017. ICIAP 2017. Lecture Notes in Computer Science, vol 10590. Springer, Cham. 10.1007/978-3-319-70742-6_46

2016

L. Bally, J. Dehais, C. Nakas, M. Anthimopoulos, M, Laimer, D. Rhyner, G. Rosenberg, Th. Züger, P. Diem, S. Mougiakakou, Ch. Stettler

Carbohydrate Estimation Supported by the GoCARB system in Individuals With Type 1 Diabetes: A Randomized Prospective Pilot Study. Diabetes care, 40(2), dc162173.

D. Rhyner, H. Loher, J. Dehais, M. Anthimopoulos, S. Shevchik, R.H. Botwey, D. Duke, C. Stettler, P. Diem, S. Mougiakakou

Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study J Med Internet Res. 2016 May 11;18(5):e101. doi: 10.2196/jmir.5567.

J. Dehais, M. Anthimopoulos, S. Mougiakakou

GoCARB: A Smartphone Application for Automatic Assessment of Carbohydrate Intake.MADiMa ’16 in conjunction with the ACM MM2016: Proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management

M. Anthimopoulos, J. Dehais, S. Mougiakakou

Performance Evaluation Methods of Computer Vision Systems for Meal Assessment. MADiMa ’16 in conjunction with the ACM MM2016: Proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management

J. Dehais, M. Anthimopoulos, S. Mougiakakou

Food Image Segmentation for Dietary Assessment. (Oral Presentation) MADiMa ’16 in conjunction with the ACM MM2016: Proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management

2015

M. Laimer, Ch. Stettler, P. Diem, S. Mougiakakou

GoCARB: Smartphone-App zur Kohlenhydratberechnung bei Typ-1-Diabetes. Der Diabetologe, 2005;11(7):575-578. Springer 10.1007/s11428-015-0026-9

J. Dehais, M. Anthimopoulos, S. Mougiakakou, Stavroula

Dish Detection and Segmentation for Dietary Assessment on Smartphones. In: Murino, Vittorio; Puppo, Enrico; Sona, Diego; Cristani, Marco; Sansone, Carlo (eds.) ICIAP 2015 Workshops. New Trends in Image Analysis and Processing. Lecture Notes in Computer Science: Vol. 9281 (pp. 433-440). Springer 10.1007/978-3-319-23222-5_53

S. Christodoulidis, M. Anthimopoulos, S. Mougiakakou

Food Recognition for Dietary Assessment Using Deep Convolutional Neural Networks. In: Murino, Vittorio; Puppo, Enrico; Sona, Diego; Cristani, Marco; Sansone, Carlo (eds.) ICIAP 2015 International Workshops. Lecture Notes in Computer Science: Vol. 9281 (pp. 458-465). Springer 10.1007/978-3-319-23222-5_56

M. Anthimopoulos, J. Dehais, S. Shevchik, R. Botwey, D. Duke, P. Diem, S. Mougiakakou

Computer Vision-Based Carbohydrate Estimation for Type 1 Diabetic Patients Using Smartphones. Journal of Diabetes Science and Technology, 2015;9(3):507-515. 10.1177/1932296815580159

A. Agianniotis, M. Anthimopoulos, E. Daskalaki, A. Drapela, C. Stettler, P. Diem, S. Mougiakakou

GoCARB in the Context of an Artificial Pancreas. Journal of Diabetes Science and Technology, 2015;9(3): 549-555. 10.1177/1932296815583333

2014

M. Anthimopoulos, L. Gianola, L. Scarnato, P. Diem, S. Mougiakakou

A Food Recognition System for Diabetic Patients based on an Optimized Bag of Features Model. IEEE Journal of Biomedical and Health Informatics, 2014;18(4):1261-2194. 10.1109/JBHI.2014.2308928

2013

M. Anthimopoulos, J. Dehais, P. Diem, S. Mougiakakou

Segmentation and Recognition of Multi-Food Meal Images for Carbohydrate Counting. In: 13th IEEE International Conference on BioInformatics and BioEngineering system (IEEE BIBE2013). Chania, Greece. 10.-13.11.2013.

J. Dehais, S. Shevchik, P. Diem, S. Mougiakakou

Food Volume Computation for Self Dietary Assessment Applications. In: 2013 IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE). Chania, Greece. 10.-13.11.2013.