New Delhi, Sep 24 || Researchers at the Indian Institute of Technology (IIT) and All India Institute of Medical Science (AIIMS) Jodhpur have leveraged the power of artificial intelligence (AI) to better identify childhood malnutrition.
The new method, published in the open-access journal MICCAI, addresses one of the most pressing global health challenges -- the accurate and scalable assessment of childhood malnutrition.
The study introduced DomainAdapt -- a novel multitasks learning framework that dynamically adjusts task weights using domain knowledge and mutual information.
This allows the system to more accurately predict key anthropometric measures such as height, weight, and mid-upper arm circumference (MUAC), while simultaneously classifying malnutrition-related conditions such as stunting, wasting, and underweight.
While these measures are also assessed using the traditional screening methods, they pose challenges in terms of the subjectivity of the worker, the time-consuming process of measuring each aspect one by one, and the lack of scalability.