Methods for Extremely Rapid Observation of Nutritional Status(MERON)

Brief Project Information

Kimetrica has developed an application called MERON. MERON is a machine learning tool - Method for Extremely Rapid Observation of Nutritional Status to detect malnutrition using photographs. It allows for a non-invasive, time efficient, and tamper-proof approach to assessing the malnutrition status of an individual by using a facial recognition and processing algorithm.

More information about the project

“MERON's next step for product development is a significant increase in its accuracy for malnutrition detection in children under-five from 60 percent to over 90 percent, which will be achieved through collecting additional image data. Doing so requires the collection of 5,000-15,000 more usable images in tandem with SMART surveys or other nutritional assessments for calibration. These benefits could, in turn, result in a number of important outcomes for the diagnosis and treatment of malnutrition in children under five. These include: 1. More appropriate distribution of funding and scarce resources based on accurate measurements. 2. Savings in resources (resources used for training enumerators to take accurate weight for height measurements; transportation of bulky equipment and opportunity cost for communities participating in surveys). 3. Easier data collection in hard to access, high risk or conflict areas, and areas where physical handling of children is culturally not acceptable. MERON was presented at the Artificial Intelligence for Good Global Summit held in Geneva in May 2018 (Watch the interview) and has been featured in the Smithsonian, New Scientist, Daily Mail and Deutsche Welle.”(According to the project website:

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A useful study that not only examines the nutritional health of infants and young children but also helps children in need get timely nutritional support.

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