New work maps a region’s nutrient landscape
Micronutrient deficiencies afflict more than two billion people worldwide, including 340 million children. This lack of vitamins and minerals can have serious health consequences. But diagnosing deficiencies early enough for effective treatment requires expensive, time-consuming blood draws and laboratory tests.
Existing AI systems can use satellite data to predict localized food security issues, but they typically rely on directly observable features. For example, agricultural productivity can be estimated from views of vegetation. Micronutrient availability is harder to calculate. After seeing research showing that areas near forests tend to have better dietary diversity, Bondi and her colleagues were inspired to identify lesser-known markers for potential malnourishment.
Predictions of regional-level micronutrient deficiency in populations outside the training data sets met, and sometimes exceeded, the accuracy of estimates based on surveys administered by local public health officials. “Our work showcases a method that allows for identification and targeting of vulnerable populations for nutritional support that may supplement ... expensive and invasive procedures,” Bondi says.
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