{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T14:10:49Z","timestamp":1779286249219,"version":"3.51.4"},"reference-count":42,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2020,7,31]],"date-time":"2020-07-31T00:00:00Z","timestamp":1596153600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"SV Stiftung","award":["N\/A"],"award-info":[{"award-number":["N\/A"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate estimation of nutritional information may lead to healthier diets and better clinical outcomes. We propose a dietary assessment system based on artificial intelligence (AI), named goFOODTM. The system can estimate the calorie and macronutrient content of a meal, on the sole basis of food images captured by a smartphone. goFOODTM requires an input of two meal images or a short video. For conventional single-camera smartphones, the images must be captured from two different viewing angles; smartphones equipped with two rear cameras require only a single press of the shutter button. The deep neural networks are used to process the two images and implements food detection, segmentation and recognition, while a 3D reconstruction algorithm estimates the food\u2019s volume. Each meal\u2019s calorie and macronutrient content is calculated from the food category, volume and the nutrient database. goFOODTM supports 319 fine-grained food categories, and has been validated on two multimedia databases that contain non-standardized and fast food meals. The experimental results demonstrate that goFOODTM performed better than experienced dietitians on the non-standardized meal database, and was comparable to them on the fast food database. goFOODTM provides a simple and efficient solution to the end-user for dietary assessment.<\/jats:p>","DOI":"10.3390\/s20154283","type":"journal-article","created":{"date-parts":[[2020,8,3]],"date-time":"2020-08-03T06:16:47Z","timestamp":1596435407000},"page":"4283","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":90,"title":["goFOODTM: An Artificial Intelligence System for Dietary Assessment"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0795-4483","authenticated-orcid":false,"given":"Ya","family":"Lu","sequence":"first","affiliation":[{"name":"ARTORG Center for Biomedical Engineering Research, University of Bern, 3008 Bern, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2847-9474","authenticated-orcid":false,"given":"Thomai","family":"Stathopoulou","sequence":"additional","affiliation":[{"name":"ARTORG Center for Biomedical Engineering Research, University of Bern, 3008 Bern, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2013-2858","authenticated-orcid":false,"given":"Maria F.","family":"Vasiloglou","sequence":"additional","affiliation":[{"name":"ARTORG Center for Biomedical Engineering Research, University of Bern, 3008 Bern, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lillian F.","family":"Pinault","sequence":"additional","affiliation":[{"name":"Division of Endocrinology, Baltimore Veterans Administration Medical Center, Baltimore, MD 21201, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Colleen","family":"Kiley","sequence":"additional","affiliation":[{"name":"Luminis Health, Anne Arundel Medical Center, Anne Arundel Medical Group Diabetes and Endocrine Specialists, Annapolis, MD 21401, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elias K.","family":"Spanakis","sequence":"additional","affiliation":[{"name":"Division of Endocrinology, Baltimore Veterans Administration Medical Center, Baltimore, MD 21201, USA"},{"name":"Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD 21201, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stavroula","family":"Mougiakakou","sequence":"additional","affiliation":[{"name":"ARTORG Center for Biomedical Engineering Research, University of Bern, 3008 Bern, Switzerland"},{"name":"Bern University Hospital \u201cInselpital\u201d, 3010 Bern, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"American Diabetes Association (2020). Cardiovascular disease and risk management: Standards of medical care in diabetes. Diabetes Care, 43, S111\u2013S134.","DOI":"10.2337\/dc20-S010"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"American Diabetes Association (2018). Economic costs of diabetes in the U.S. in 2017. Diabetes Care, 41, 917\u2013928.","DOI":"10.2337\/dci18-0007"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e21","DOI":"10.1111\/j.1464-5491.2012.03595.x","article-title":"In children using intensive insulin therapy, a 20-g variation in carbohydrate amount significantly impacts on postprandial glycaemia","volume":"29","author":"Smart","year":"2012","journal-title":"Diabet. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.diabres.2012.10.024","article-title":"Carbohydrate counting accuracy and blood glucose variability in adults with type 1 diabetes","volume":"99","author":"Brazeau","year":"2013","journal-title":"Diabetes Res. Clin. Pract."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"56","DOI":"10.2337\/diaspect.22.1.56","article-title":"The carbohydrate counting in adolescents with Type 1 Diabetes (CCAT) Study","volume":"22","author":"Franziska","year":"2009","journal-title":"Diabetes Spectr."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Smart, C.E., Ross, K., Edge, J.A., King, B.R., McElduff, P., and Collins, C.E. (2009). Can children with type 1 diabetes and their caregivers estimate the carbohydrate content of meals and snacks?. Diabet. Med.","DOI":"10.1111\/j.1464-5491.2009.02945.x"},{"key":"ref_7","unstructured":"World Health Organization (WHO) (2020, July 22). Diet, Nutrition and the Prevention of Chronic Diseases. Available online: https:\/\/www.who.int\/dietphysicalactivity\/publications\/trs916\/en\/."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Voulodimos, A., Doulamis, N., Doulamis, A., and Protopapadakis, E. (2018). Deep learning for computer vision: A brief review. Comput. Intell. Neurosci., 2018.","DOI":"10.1155\/2018\/7068349"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.neucom.2015.09.116","article-title":"Deep learning for visual understanding: A review","volume":"187","author":"Guo","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_10","unstructured":"Leo, M., Furnari, A., Medioni, G.G., Trivedi, M., and Farinella, G.M. (2014, January 6\u201312). Deep learning for assistive computer vision. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.1109\/JBHI.2014.2308928","article-title":"A food recognition system for diabetic patients based on an optimized bag of features model","volume":"18","author":"Anthimopoulos","year":"2014","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1109\/JBHI.2016.2636441","article-title":"Food recognition: A new dataset, experiments and results","volume":"21","author":"Ciocca","year":"2016","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1109\/TIP.2019.2929447","article-title":"Multi-scale multi-view deep feature aggregation for food recognition","volume":"29","author":"Jiang","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Meyers, A., Johnston, N., Rathod, V., Korattikara, A., Gorban, A., Silberman, N., Guadarrama, S., Papandreou, G., Huang, J., and Murphy, K.P. (2015, January 7\u201313). Im2Calories: Towards an Automated Mobile Vision Food Diary. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.146"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"dc162173","DOI":"10.2337\/dc16-2173","article-title":"Carbohydrate estimation supported by the GoCARB system in individuals with type 1 diabetes: A randomized prospective pilot study","volume":"40","author":"Bally","year":"2017","journal-title":"Diabetes Care"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bossard, L. (2014, January 6\u201312). Food-101\u2014Mining discriminative components with random forests. Proceedings of the European Conference on Computer Vision (ECCV), Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10599-4_29"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kawano, Y., and Yanai, K. (2014, January 10\u201316). Automatic expansion of a food image dataset leveraging existing categories with domain adaptation. Proceedings of the European Conference on Computer Vision Workshop (ECCVW), Rhodes, Greece.","DOI":"10.1007\/978-3-319-16199-0_1"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Puri, M., Zhu, Z., Yu, Q., Divakaran, A., and Sawhney, H. (2009, January 7\u20138). Recognition and volume estimation of food intake using a mobile device. Proceedings of the IEEE Workshop on Applications of Computer Vision, Snowbird, UT, USA.","DOI":"10.1109\/WACV.2009.5403087"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.pmcj.2011.07.003","article-title":"DietCam: Automatic dietary assessment with mobile camera phones","volume":"8","author":"Kong","year":"2012","journal-title":"J. Pervasive Mob. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1090","DOI":"10.1109\/TMM.2016.2642792","article-title":"Two-view 3D reconstruction for food volume estimation","volume":"19","author":"Dehais","year":"2017","journal-title":"IEEE Trans. Multimed."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Vasiloglou, M.F., Mougiakakou, S., Aubry, E., Bokelmann, A., Fricker, R., Gomes, F., Guntermann, C., Meyer, A., Studerus, D., and Stanga, Z. (2018). A comparative study on carbohydrate estimation: GoCARB vs. Dietitians. Nutrients, 10.","DOI":"10.3390\/nu10060741"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1177\/1932296815580159","article-title":"Computer vision-based carbohydrate estimation for type 1 diabetic patients using smartphones","volume":"9","author":"Anthimopoulos","year":"2015","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e101","DOI":"10.2196\/jmir.5567","article-title":"Carbohydrate estimation by a mobile phone-based system versus self-estimations of individuals with type 1 diabetes mellitus: A comparative study","volume":"18","author":"Rhyner","year":"2016","journal-title":"J. Med. Internet Res. JMIR"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ege, T., and Yanai, K. (2017, January 23\u201327). Image-based food calorie estimation using knowledge on food categories, ingredients and cooking directions. Proceedings of the on Thematic Workshops of ACM Multimedia, Mountain View, CA, USA.","DOI":"10.1145\/3126686.3126742"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Fang, S., Shao, Z., Mao, R., Fu, C., Kerr, D.A., Boushey, C.J., Delp, E.J., and Zhu, F. (2018, January 7\u201310). Single-view food portion estimation: Learning image-to-energy mappings using generative adversarial networks. Proceedings of the IEEE International Conference on Image Processing, Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451461"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lu, Y., Allegra, D., Anthimopoulos, M., Stanco, F., Farinella, G.M., and Mougiakakou, S. (2018, January 15). A multi-task learning approach for meal assessment. Proceedings of the Joint Workshop on Multimedia for Cooking and Eating Activities and Multimedia Assisted Dietary Management (CEA\/MADiMa \u201918), Stockholm, Sweden.","DOI":"10.1145\/3230519.3230593"},{"key":"ref_27","unstructured":"(2020, July 22). FatSecret. Available online: https:\/\/www.fatsecret.com."},{"key":"ref_28","unstructured":"(2020, July 22). CALORIE MAMA. Available online: https:\/\/dev.caloriemama.ai."},{"key":"ref_29","unstructured":"(2020, July 22). Bitesnap. Available online: https:\/\/getbitesnap.com."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Allegra, D., Anthimopoulos, M., Dehais, J., Lu, Y., Stanco, F., Farinella, G.M., and Mougiakakou, S. (2017, January 11\u201315). A multimedia database for automatic meal assessment systems. Proceedings of the International Conference on Image Analysis and Processing (ICIAP), Catania, Italy.","DOI":"10.1007\/978-3-319-70742-6_46"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Hassannejad, H., Matrella, G., Ciampolini, P., De Munari, I., Mordonini, M., and Cagnoni, S. (2016, January 16). Food image recognition using very deep convolutional networks. Proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management, Amsterdam, The Netherlands.","DOI":"10.1145\/2986035.2986042"},{"key":"ref_34","unstructured":"Kaur, P., Sikka, K., Wang, W., Belongie, S., and Divakaran, A. (2019, January 16\u201320). FoodX-251: A Dataset for Fine-grained Food Classification. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition Workshop (CVPRW), Long Beach, CA, USA."},{"key":"ref_35","unstructured":"(2020, July 22). Nutritionix Database. Available online: https:\/\/www.nutritionix.com\/database."},{"key":"ref_36","unstructured":"(2020, July 22). U.S. Department of Agriculture: FoodData Central, Available online: https:\/\/ndb.nal.usda.gov\/ndb\/."},{"key":"ref_37","unstructured":"(2020, July 22). Swiss Food Composition Database. Available online: https:\/\/www.naehrwertdaten.ch\/en\/."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Dehais, J., Anthimopoulos, M., and Mougiakakou, S. (2015, January 7\u20138). Dish Detection and Segmentation for Dietary Assessment on Smartphones. Proceedings of the 8th International Conference on Image Analysis and Processing (ICIAP2015), Genoa, Italy.","DOI":"10.1007\/978-3-319-23222-5_53"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Dehais, J., Anthimopoulos, M., and Mougiakakou, S. (2016, January 16). Food image segmentation for dietary assessment. Proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management, Amsterdam, The Netherlands.","DOI":"10.1145\/2986035.2986047"},{"key":"ref_40","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 12\u201315). ImageNet classification with deep convolutional neural networks. Proceedings of the International Conference on Neural Information Processing Systems, Doha, Qatar."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1109\/7.303741","article-title":"Synergism of binocular and motion stereo for passive ranging","volume":"30","author":"Bhanu","year":"1994","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_42","unstructured":"(2020, July 22). Aical-Photo & Voice Calories Counter. Available online: https:\/\/apps.apple.com\/gb\/app\/aical-calories-counter\/id1484771102."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/15\/4283\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:53:23Z","timestamp":1760176403000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/15\/4283"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,31]]},"references-count":42,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2020,8]]}},"alternative-id":["s20154283"],"URL":"https:\/\/doi.org\/10.3390\/s20154283","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,31]]}}}