{"type":"ScholarlyArticle","title":"NutriAI: AI-Powered Child Malnutrition Assessment in Low-Resource Environments","authors":[{"name":"Misaal Khan","slug":"misaal-khan","given":"Misaal","family":"Khan","affiliation":"Indian Institute of Technology Jodhpur , India; All India Institute of Medical Science Jodhpur , India"},{"name":"Shivang Agarwal","slug":"shivang-agarwal","given":"Shivang","family":"Agarwal","affiliation":"Indian Institute of Technology Jodhpur , India"},{"name":"Mayank Vatsa","slug":"mayank-vatsa","given":"Mayank","family":"Vatsa","affiliation":"Indian Institute of Technology Jodhpur , India"},{"name":"Richa Singh","slug":"richa-singh","given":"Richa","family":"Singh","affiliation":"Indian Institute of Technology Jodhpur , India"},{"name":"Kuldeep Singh","slug":"kuldeep-singh","given":"Kuldeep","family":"Singh","affiliation":"All India Institute of Medical Science Jodhpur , India"}],"abstract":"Malnutrition among infants and young children is a pervasive public health concern, particularly in developing countries where resources are limited. Millions of children globally suffer from malnourishment and its complications1. Despite the best efforts of governments and organizations, malnourishment persists and remains a leading cause of morbidity and mortality among children under five. Physical measurements, such as weight, height, middle-upper-arm-circumference (muac), and head circumference are commonly used to assess the nutritional status of children. However, this approach can be resource-intensive and challenging to carry out on a large scale. In this research, we are developing NutriAI, a low-cost solution that leverages\r\nsmall sample size classification approach to detect malnutrition by analyzing 2D images of the subjects in multiple poses. The proposed solution will not only reduce the workload of health workers but also provide a more efficient means of monitoring the nutritional status of children. On the dataset prepared as part of this research, the baseline results highlight that the modern deep learning approaches can facilitate malnutrition detection via anthropometric indicators in the presence of diversity with respect to age, gender, physical characteristics, and accessories including clothing.","doi":"10.24963/ijcai.2023/708","url":"https://ijcai.org/proceedings/2023/708","pdf":"https://ijcai.org/proceedings/2023/0708.pdf","markdown":"https://ijcai.org/proceedings/2023/708.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/708","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"AI for Good - Projects","track":"AI for Good - Projects","number":708,"pages":{"first":6378,"last":6385},"keywords":["AI for Good - Projects: Computer Vision","AI for Good - Projects: Machine Learning"],"videos":[],"references":[{"position":1,"raw":"[Black et al., 2008] Robert E Black, Lindsay H Allen, Zul- fiqar A Bhutta, Laura E Caulfield, Mercedes De Onis, Ma- jid Ezzati, Colin Mathers, Juan Rivera, Maternal, Child Undernutrition Study Group, et al. Maternal and child undernutrition: global and regional exposures and health consequences. The lancet, 371(9608):243-260, 2008.","title":"Maternal and child undernutrition: global and regional exposures and health consequences","authors":"Robert E Black, Zulfiqar A Lindsay H Allen, Laura E Bhutta, Mercedes De Caulfield, Majid Onis, Colin Ezzati, Juan Mathers, Rivera, Child Maternal, Undernutrition Study, Group","year":"2008","venue":"The lancet","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Blacklaws, 2018] Christina Blacklaws. Algorithms: trans- parency and accountability. Philosophical Transactions of the Royal Society A: Mathematical, Physical and En- gineering Sciences, 376(2128):20170351, 2018.","title":"Algorithms: transparency and accountability","authors":"Blacklaws","year":"2018","venue":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[C et al., 2022] Dhanamjayulu C, Nizhal U N, Praveen Ku- mar Reddy Maddikunta, Thippa Reddy Gadekallu, Celes- tine Iwendi, Chuliang Wei, and Qin Xin. Identification of malnutrition and prediction of bmi from facial images us- ing real-time image processing and machine learning. IET Image Processing, 16(3):647-658, 2022.","title":"Identification of malnutrition and prediction of bmi from facial images using real-time image processing and machine learning","authors":"C","year":"2022","venue":"IET Image Processing","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Dukhi, 2020] Natisha Dukhi. Global prevalence of malnu- trition: evidence from literature. In Malnutrition. Inte- chOpen, 2020.","title":"Global prevalence of malnutrition: evidence from literature","authors":"Natisha Dukhi, Dukhi","year":"2020","venue":"Malnutrition. Inte-chOpen","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Grantham-McGregor, 1995] Sally Grantham-McGregor. A review of studies of the effect of severe malnutrition on mental development. The Journal of nutrition, 125:2233S-2238S, 1995.","title":"A review of studies of the effect of severe malnutrition on mental development","authors":"Sally Grantham-Mcgregor","year":"1995","venue":"The Journal of nutrition","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[He et al., 2016] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recog- nition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.","title":"Deep residual learning for image recognition","authors":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun","year":"2016","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Heemann et al., 2021] Markus Heemann, Rockli Kim, Se- bastian Vollmer, and SV Subramanian. Assessment of undernutrition among children in 55 low-and middle- income countries using dietary and anthropometric mea- sures. JAMA Network Open, 4(8):e2120627-e2120627, 2021.","title":"Assessment of undernutrition among children in 55 low-and middleincome countries using dietary and anthropometric measures","authors":"Heemann","year":"2021","venue":"JAMA Network Open","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Huang et al., 2017] Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely connected convolutional networks. In Proceedings of the IEEE con- ference on computer vision and pattern recognition, pages 4700-4708, 2017.","title":"Densely connected convolutional networks","authors":"Gao Huang, Zhuang Liu, Laurens Van Der Maaten, Kilian Q Weinberger","year":"2017","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Kendall et al., 2018] Alex Kendall, Yarin Gal, and Roberto Cipolla. Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7482-7491, 2018.","title":"Multi-task learning using uncertainty to weigh losses for scene geometry and semantics","authors":"Alex Kendall, Yarin Gal, Roberto Cipolla","year":"2018","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Khan et al., 2022] Misaal Khan, Mahapara Khurshid, Mayank Vatsa, Richa Singh, Mona Duggal, and Kuldeep Singh. On ai approaches for promoting maternal and neonatal health in low resource settings: A review. Frontiers in Public Health, 10:1864, 2022.","title":"On ai approaches for promoting maternal and neonatal health in low resource settings: A review","authors":"Khan","year":"2022","venue":"Frontiers in Public Health","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Kuczmarski, 2002] Robert J Kuczmarski. 2000 CDC Growth Charts for the United States: methods and devel- opment. Number 246. Department of Health and Human Services, Centers for Disease Control and Prevention, Na- tional Center for Health Statistics, 2002.","title":"CDC Growth Charts for the United States: methods and development","authors":"J Robert, Kuczmarski","year":"2000","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Lie et al., 2004] RK Lie, E Emanuel, C Grady, and D Wendler. The helsinki declaration, 2000, and ethics of human research in developing countries. Indian Journal of Medical Ethics, 1(3), 2004.","title":"The helsinki declaration, 2000, and ethics of human research in developing countries","authors":"Rk Lie, C Emanuel, Grady, Wendler","year":"2004","venue":"Indian Journal of Medical Ethics","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Mathur et al., 2018] Roli Mathur, Soumya Swaminathan, et al. National ethical guidelines for biomedical & health research involving human participants, 2017: A commen- tary. The Indian journal of medical research, 148(3):279, 2018.","title":"National ethical guidelines for biomedical & health research involving human participants, 2017: A commentary","authors":"Mathur","year":"2018","venue":"The Indian journal of medical research","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Ogden et al., 2002] Cynthia L Ogden, Robert J Kucz- marski, Katherine M Flegal, Zuguo Mei, Shumei Guo, Rong Wei, Laurence M Grummer-Strawn, Lester R Curtin, Alex F Roche, and Clifford L Johnson. Centers for disease control and prevention 2000 growth charts for the united states: improvements to the 1977 national center for health statistics version. Pediatrics, 109(1):45-60, 2002.","title":"Centers for disease control and prevention 2000 growth charts for the united states: improvements to the 1977 national center for health statistics version","authors":"Cynthia L Ogden, Robert J Kuczmarski, Katherine M Flegal, Zuguo Mei, Shumei Guo, Rong Wei, Laurence M Grummer-Strawn, Alex F Lester R Curtin, Clifford L Roche, Johnson","year":"2002","venue":"Pediatrics","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Riddick, 2003] Frank A Riddick. The code of medical ethics of the american medical association. Ochsner Jour- nal, 5(2):6-10, 2003.","title":"The code of medical ethics of the american medical association","authors":"A Frank, Riddick","year":"2003","venue":"Ochsner Journal","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Sethi et al., 2020] Ashish Sethi, Mayank Vatsa, and Richa Singh. Detecting Malnourishment in Children: Small Sample Size Problem in Deep Learning World. Mtech the- sis, IIIT-D-MTech-CS-AI-20-MT18024, 2020.","title":"Detecting Malnourishment in Children: Small Sample Size Problem in Deep Learning World","authors":"Ashish Sethi, Mayank Vatsa, Richa Singh","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Shrimpton et al., 2001] Roger Shrimpton, Cesar G Victora, Mercedes de Onis, Rosangela Costa Lima, Monika Bloss- ner, and Graeme Clugston. Worldwide timing of growth faltering: implications for nutritional interventions. Pedi- atrics, 107(5):e75-e75, 2001.","title":"Worldwide timing of growth faltering: implications for nutritional interventions","authors":"Roger Shrimpton, Cesar G Victora, Mercedes De Onis, Rosangela, Costa Lima, Monika Blossner, Graeme Clugston","year":"2001","venue":"Pediatrics","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Simonyan and Zisserman, 2015] Karen Simonyan and An- drew Zisserman. Very deep convolutional networks for large-scale image recognition. In Yoshua Bengio and Yann LeCun, editors, 3rd International Conference on Learning Representations, ICLR,, 2015.","title":"Very deep convolutional networks for large-scale image recognition","authors":"Karen Simonyan, Andrew Zisserman","year":"2015","venue":"3rd International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Wang et al., 2020] Yaqing Wang, Quanming Yao, James T Kwok, and Lionel M Ni. Generalizing from a few ex- amples: A survey on few-shot learning. ACM computing surveys (csur), 53(3):1-34, 2020.","title":"Generalizing from a few examples: A survey on few-shot learning","authors":"Wang","year":"2020","venue":"ACM computing surveys (csur)","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[WHO, 1995] WHO. Physical status: The use of and in- terpretation of anthropometry, Report of a WHO Expert Committee. World Health Organization, 1995.","title":"WHO. Physical status: The use of and interpretation of anthropometry, Report of a WHO Expert Committee","authors":null,"year":"1995","venue":null,"doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[WHO, 2021] WHO. Ethics and governance of artificial in- telligence for health: Who guidance. 2021.","title":"WHO. Ethics and governance of artificial intelligence for health: Who guidance","authors":null,"year":"2021","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}