University researchers working with the Spanish affiliate of international non-governmental aid agency Action Against Hunger (ACT International) have developed a breakthrough mobile photo application that they say will enable medical aid workers to quickly and accurately diagnose malnutrition in children.
The SAM photo diagnosis application, developed by a binational team from Madrid’s Complutense University and Dakar’s Cheik Anta Diop University at the behest of the international NGO’s Acción contra el Hambre Spain-based affiliate, relies on advanced geometric morphometrics imaging technology never before used on human beings to compare the shape of a healthy child with the photograph of an undernourished child to determine the level of malnutrition in real-time in the field.
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The app uses Cartesian geometric coordinates, rather than the usual linear and volumetric measurements of size, height and weight, to gain a more accurate reading of malnutrition in children under the age of five years. Once deployed widely, the app will enable medical aid workers to obtain an accurate, real-time assessment of the level of child malnutrition in the field, particularly in remote areas of the world or conflict zones where children are at high risk of suffering undernourishment and starvation to increases their vulnerability to accompanying disease.
After testing on hundreds of children in a pilot program in Senegal and Spain, the app received the distinction of Best Social Digital Innovation Project from Spain’s Mutua Madrileña Foundation earlier this month. The results of the pilot testing program in Senegal and Spain have been validated through positive peer review in an article in the prestigious American Journal of Physical Anthropology.
The next step is to make the technology scalable for widespread use by aid workers and to integrate the mobile app into programs for follow-up treatment, helping medical aid staff in the field prescribe a nutrition program to ensure a child’s recovery from malnutrition and guard against potential relapse.
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