首页|Studies from Tulane University Yield New Information about Artificial Intelligence (Artificial Intelligence for the Practical Assessment of Nutritional Status In Emergencies)
Studies from Tulane University Yield New Information about Artificial Intelligence (Artificial Intelligence for the Practical Assessment of Nutritional Status In Emergencies)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Wiley
Investigators publish new report on Artificial Intelligence. According to news reporting originating from New Orleans, Louisiana, by NewsRx correspondents, research stated, "This paper describes a novel method for detecting child malnutrition based on artificial intelligence and facial photography. Estimates of severe and moderate acute malnutrition in children are critical for rapid emergency responses." Financial support for this research came from UNICEF. Our news editors obtained a quote from the research from Tulane University, "However, the two traditional measurement methods, mid-upper arm circumference (MUAC) and weight-for-height (WFH), are impractical in conflict and catastrophic disaster situations. They require well-trained enumerators, cumbersome equipment, and close supervision. The Method for Extremely Rapid Observation of Nutritional Status (MERON) addresses the problem, using simple facial photographs. Facial features are extracted to predict Body Mass Index (BMI) in adults and Weight for Height Z Score (WFHZ) in children under five. MERON correctly predicts adult BMI classification with 78% accuracy. A variant of the model, trained on a sample of 3167 children in Kenya, successfully classified 60% of cases. On most measures, MERON was easier and more culturally acceptable to use than the traditional measurement methods."
New OrleansLouisianaUnited StatesNorth and Central AmericaArtificial IntelligenceEmerging TechnologiesMachine LearningTulane University
NETL
NSTL
Wiley
