{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T01:29:17Z","timestamp":1760318957821,"version":"3.40.3"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031442391"},{"type":"electronic","value":"9783031442407"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-44240-7_8","type":"book-chapter","created":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T22:01:45Z","timestamp":1695160905000},"page":"77-86","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Complete AI-Based System for\u00a0Dietary Assessment and\u00a0Personalized Insulin Adjustment in\u00a0Type 1 Diabetes Self-management"],"prefix":"10.1007","author":[{"given":"Maria","family":"Panagiotou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ioannis","family":"Papathanail","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lubnaa","family":"Abdur Rahman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lorenzo","family":"Brigato","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Natalie S.","family":"Bez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria F.","family":"Vasiloglou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomai","family":"Stathopoulou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bastiaan E.","family":"de Galan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ulrik","family":"Pedersen-Bjergaard","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Klazine","family":"van der Horst","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stavroula","family":"Mougiakakou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,20]]},"reference":[{"issue":"3","key":"8_CR1","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1177\/1932296815583333","volume":"9","author":"A Agianniotis","year":"2015","unstructured":"Agianniotis, A., et al.: Gocarb in the context of an artificial pancreas. J. Diabetes Sci. Technol. 9(3), 549\u2013555 (2015)","journal-title":"J. Diabetes Sci. Technol."},{"key":"8_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1007\/978-3-319-70742-6_46","volume-title":"New Trends in Image Analysis and Processing \u2013 ICIAP 2017","author":"D Allegra","year":"2017","unstructured":"Allegra, D., et al.: A multimedia database for automatic meal assessment systems. In: Battiato, S., Farinella, G.M., Leo, M., Gallo, G. (eds.) ICIAP 2017. LNCS, vol. 10590, pp. 471\u2013478. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-70742-6_46"},{"issue":"4","key":"8_CR3","doi-asserted-by":"publisher","first-page":"1261","DOI":"10.1109\/JBHI.2014.2308928","volume":"18","author":"MM Anthimopoulos","year":"2014","unstructured":"Anthimopoulos, M.M., Gianola, L., Scarnato, L., Diem, P., Mougiakakou, S.G.: A food recognition system for diabetic patients based on an optimized bag-of-features model. IEEE J. Biomed. Health Inform. 18(4), 1261\u20131271 (2014)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"8_CR4","doi-asserted-by":"crossref","unstructured":"Association, A.D.: Diagnosis and classification of diabetes mellitus. Diabetes care 33(Suppl._1), S62\u2013S69 (2010)","DOI":"10.2337\/dc10-S062"},{"issue":"2","key":"8_CR5","doi-asserted-by":"publisher","first-page":"e6","DOI":"10.2337\/dc16-2173","volume":"40","author":"L Bally","year":"2017","unstructured":"Bally, L., et al.: Carbohydrate estimation supported by the gocarb system in individuals with type 1 diabetes: a randomized prospective pilot study. Diabetes Care 40(2), e6\u2013e7 (2017)","journal-title":"Diabetes Care"},{"key":"8_CR6","doi-asserted-by":"crossref","unstructured":"Daskalaki, E., Diem, P., Mougiakakou, S.G.: Personalized tuning of a reinforcement learning control algorithm for glucose regulation. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3487\u20133490. IEEE (2013)","DOI":"10.1109\/EMBC.2013.6610293"},{"issue":"7","key":"8_CR7","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0158722","volume":"11","author":"E Daskalaki","year":"2016","unstructured":"Daskalaki, E., Diem, P., Mougiakakou, S.G.: Model-free machine learning in biomedicine: feasibility study in type 1 diabetes. PLoS ONE 11(7), e0158722 (2016)","journal-title":"PLoS ONE"},{"issue":"5","key":"8_CR8","doi-asserted-by":"publisher","first-page":"1090","DOI":"10.1109\/TMM.2016.2642792","volume":"19","author":"J Dehais","year":"2016","unstructured":"Dehais, J., Anthimopoulos, M., Shevchik, S., Mougiakakou, S.: Two-view 3D reconstruction for food volume estimation. IEEE Trans. Multimedia 19(5), 1090\u20131099 (2016)","journal-title":"IEEE Trans. Multimedia"},{"key":"8_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"8_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.105936","volume":"200","author":"A Jafar","year":"2021","unstructured":"Jafar, A., El Fathi, A., Haidar, A.: Long-term use of the hybrid artificial pancreas by adjusting carbohydrate ratios and programmed basal rate: a reinforcement learning approach. Comput. Methods Programs Biomed. 200, 105936 (2021)","journal-title":"Comput. Methods Programs Biomed."},{"key":"8_CR11","doi-asserted-by":"publisher","DOI":"10.3389\/fnut.2020.519444","volume":"7","author":"W Jia","year":"2021","unstructured":"Jia, W., Wu, Z., Ren, Y., Cao, S., Mao, Z.H., Sun, M.: Estimating dining plate size from an egocentric image sequence without a fiducial marker. Front. Nutr. 7, 519444 (2021)","journal-title":"Front. Nutr."},{"key":"8_CR12","doi-asserted-by":"publisher","first-page":"5263","DOI":"10.1007\/s11042-014-2000-8","volume":"74","author":"Y Kawano","year":"2015","unstructured":"Kawano, Y., Yanai, K.: Foodcam: a real-time food recognition system on a smartphone. Multimedia Tools Appl. 74, 5263\u20135287 (2015)","journal-title":"Multimedia Tools Appl."},{"key":"8_CR13","unstructured":"Li, J., Socher, R., Hoi, S.C.: Dividemix: learning with noisy labels as semi-supervised learning. arXiv preprint arXiv:2002.07394 (2020)"},{"issue":"1","key":"8_CR14","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1109\/TII.2019.2942831","volume":"16","author":"FPW Lo","year":"2019","unstructured":"Lo, F.P.W., Sun, Y., Qiu, J., Lo, B.P.: Point2volume: a vision-based dietary assessment approach using view synthesis. IEEE Trans. Industr. Inf. 16(1), 577\u2013586 (2019)","journal-title":"IEEE Trans. Industr. Inf."},{"key":"8_CR15","unstructured":"Louis, M., Ugalde, H.R., Gauthier, P., Adenis, A., Tourki, Y., Huneker, E.: Safe reinforcement learning for automatic insulin delivery in type i diabetes. In: Reinforcement Learning for Real Life Workshop, NeurIPS 2022 (2022)"},{"key":"8_CR16","doi-asserted-by":"crossref","unstructured":"Lu, Y., Allegra, D., Anthimopoulos, M., Stanco, F., Farinella, G.M., Mougiakakou, S.: 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, pp. 46\u201352 (2018)","DOI":"10.1145\/3230519.3230593"},{"issue":"15","key":"8_CR17","doi-asserted-by":"publisher","first-page":"4283","DOI":"10.3390\/s20154283","volume":"20","author":"Y Lu","year":"2020","unstructured":"Lu, Y., et al.: goFOODTM: an artificial intelligence system for dietary assessment. Sensors 20(15), 4283 (2020)","journal-title":"Sensors"},{"key":"8_CR18","doi-asserted-by":"crossref","unstructured":"Meyers, A., et al.: Im2calories: towards an automated mobile vision food diary. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1233\u20131241 (2015)","DOI":"10.1109\/ICCV.2015.146"},{"issue":"7","key":"8_CR19","doi-asserted-by":"publisher","first-page":"657","DOI":"10.3390\/nu9070657","volume":"9","author":"S Mezgec","year":"2017","unstructured":"Mezgec, S., Korou\u0161i\u0107 Seljak, B.: Nutrinet: a deep learning food and drink image recognition system for dietary assessment. Nutrients 9(7), 657 (2017)","journal-title":"Nutrients"},{"issue":"12","key":"8_CR20","doi-asserted-by":"publisher","first-page":"4539","DOI":"10.3390\/nu13124539","volume":"13","author":"I Papathanail","year":"2021","unstructured":"Papathanail, I., et al.: Evaluation of a novel artificial intelligence system to monitor and assess energy and macronutrient intake in hospitalised older patients. Nutrients 13(12), 4539 (2021)","journal-title":"Nutrients"},{"issue":"1","key":"8_CR21","doi-asserted-by":"publisher","first-page":"17008","DOI":"10.1038\/s41598-022-21421-y","volume":"12","author":"I Papathanail","year":"2022","unstructured":"Papathanail, I., et al.: A feasibility study to assess mediterranean diet adherence using an AI-powered system. Sci. Rep. 12(1), 17008 (2022)","journal-title":"Sci. Rep."},{"key":"8_CR22","doi-asserted-by":"crossref","unstructured":"Pouladzadeh, P., Kuhad, P., Peddi, S.V.B., Yassine, A., Shirmohammadi, S.: Food calorie measurement using deep learning neural network. In: 2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings, pp. 1\u20136. IEEE (2016)","DOI":"10.1109\/I2MTC.2016.7520547"},{"key":"8_CR23","doi-asserted-by":"crossref","unstructured":"Radosavovic, I., Kosaraju, R.P., Girshick, R., He, K., Doll\u00e1r, P.: Designing network design spaces. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10428\u201310436 (2020)","DOI":"10.1109\/CVPR42600.2020.01044"},{"issue":"5","key":"8_CR24","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.5567","volume":"18","author":"D Rhyner","year":"2016","unstructured":"Rhyner, D., et al.: 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. 18(5), e101 (2016)","journal-title":"J. Med. Internet Res."},{"issue":"5","key":"8_CR25","doi-asserted-by":"publisher","first-page":"1035","DOI":"10.1177\/1932296814532906","volume":"8","author":"S Schmidt","year":"2014","unstructured":"Schmidt, S., N\u00f8rgaard, K.: Bolus calculators. J. Diabetes Sci. Technol. 8(5), 1035\u20131041 (2014)","journal-title":"J. Diabetes Sci. Technol."},{"issue":"6","key":"8_CR26","doi-asserted-by":"publisher","first-page":"2633","DOI":"10.1109\/JBHI.2018.2887067","volume":"23","author":"Q Sun","year":"2018","unstructured":"Sun, Q., et al.: A dual mode adaptive basal-bolus advisor based on reinforcement learning. IEEE J. Biomed. Health Inform. 23(6), 2633\u20132641 (2018)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"8_CR27","doi-asserted-by":"crossref","unstructured":"Sun, Q., Jankovic, M.V., Mougiakakou, S.G.: Reinforcement learning-based adaptive insulin advisor for individuals with type 1 diabetes patients under multiple daily injections therapy. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3609\u20133612. IEEE (2019)","DOI":"10.1109\/EMBC.2019.8857178"},{"key":"8_CR28","doi-asserted-by":"crossref","unstructured":"Thames, Q., et al.: Nutrition5k: towards automatic nutritional understanding of generic food. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8903\u20138911 (2021)","DOI":"10.1109\/CVPR46437.2021.00879"},{"issue":"1","key":"8_CR29","doi-asserted-by":"publisher","DOI":"10.2196\/24467","volume":"9","author":"MF Vasiloglou","year":"2021","unstructured":"Vasiloglou, M.F., et al.: The human factor in automated image-based nutrition apps: analysis of common mistakes using the gofood lite app. JMIR Mhealth Uhealth 9(1), e24467 (2021)","journal-title":"JMIR Mhealth Uhealth"},{"key":"8_CR30","doi-asserted-by":"crossref","unstructured":"Vasiloglou, M.F., et al.: A comparative study on carbohydrate estimation: gocarb vs. dietitians. Nutrients 10(6), 741 (2018)","DOI":"10.3390\/nu10060741"},{"key":"8_CR31","doi-asserted-by":"crossref","unstructured":"Wu, X., Fu, X., Liu, Y., Lim, E.P., Hoi, S.C., Sun, Q.: A large-scale benchmark for food image segmentation. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 506\u2013515 (2021)","DOI":"10.1145\/3474085.3475201"},{"issue":"4","key":"8_CR32","doi-asserted-by":"publisher","first-page":"1223","DOI":"10.1109\/JBHI.2020.3014556","volume":"25","author":"T Zhu","year":"2020","unstructured":"Zhu, T., Li, K., Herrero, P., Georgiou, P.: Basal glucose control in type 1 diabetes using deep reinforcement learning: an in silico validation. IEEE J. Biomed. Health Inform. 25(4), 1223\u20131232 (2020)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"18","key":"8_CR33","doi-asserted-by":"publisher","first-page":"5058","DOI":"10.3390\/s20185058","volume":"20","author":"T Zhu","year":"2020","unstructured":"Zhu, T., Li, K., Kuang, L., Herrero, P., Georgiou, P.: An insulin bolus advisor for type 1 diabetes using deep reinforcement learning. Sensors 20(18), 5058 (2020)","journal-title":"Sensors"}],"container-title":["Lecture Notes in Computer Science","Computer Analysis of Images and Patterns"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44240-7_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T22:04:22Z","timestamp":1695161062000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44240-7_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031442391","9783031442407"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44240-7_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"20 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CAIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer Analysis of Images and Patterns","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Limassol","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cyprus","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"caip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cyprusconferences.org\/caip2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"https:\/\/www.easyacademia.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"67","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"54","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"81% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.06","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.09","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}