Saturday September 21, 2019

AI App With Microsoft Azure to Tackle Malnutrition in India

The new app will hugely impact the early identification of children suffering from malnutrition, thereby reducing the treatment time

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Sanjay Mehta
The children of families in Saranda, Jharkhand suffer from malnutrition and malaria. Sanjay Mehta

Germany-based non-profit Welthungerhilfe has developed an Artificial Intelligence (AI) smartphone app, powered by Microsoft Azure, to tackle malnutrition in India.

The Child Growth Monitor — a cloud-based app powered by Microsoft Azure and AI services — can detect malnutrition and enable health workers to identify and provide care to children suffering from chronic undernourishment.

By March, the app will help health workers scan 10,000 children under the age of five for signs of malnutrition, across Maharashtra, Madhya Pradesh and Rajasthan, the company said in a statement on Thursday.

Twelve teams of 150 trained health workers have been provided app-enabled smartphones to collect the data of children, it added.

The app uses an infrared sensor available in some smartphones to capture 3D measurements of a child’s height, body volume and weight ratio, as well as head and upper arm circumferences down to the millimetre.

Representational image showing a malnutrition ridden child.

The app loads that captured data into Microsoft Azure. Nutritionists and IT specialists then evaluate the scans by using Microsoft AI solutions, pinpointing a child’s dietary health.

“Today, more than 800 million people around the world suffer from hunger. You can’t solve hunger if you don’t know where the hungry people are,” said Jochen Moninger, Innovation Director at the Welthungerhilfe.

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“The Child Growth Monitor app will emerge as a recognised, global solution among humanitarian organisations. In India alone, that could free up hundreds of millions of dollars for reinvestment into the lives of children,” he added.

The new app will hugely impact the early identification of children suffering from malnutrition, thereby reducing the treatment time. (IANS)

Next Story

Researchers Can Now Detect Heart Failure with 100% Accuracy with the Help of Artificial Intelligence

Conversely, their new model uses a combination of advanced signal processing and machine learning tools on raw ECG signals, delivering 100 per cent accuracy

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Artificial Intelligence Bot
Artificial Intelligence Bot. Pixabay

With the help of Artificial Intelligence(AI), researchers have developed a neural network approach that can accurately identify congestive heart failure with 100 per cent accuracy through analysis of just one raw electrocardiogram (ECG) heartbeat.

Congestive heart failure (CHF) is a chronic progressive condition that affects the pumping power of the heart muscles. Associated with high prevalence, significant mortality rates and sustained healthcare costs, clinical practitioners and health systems urgently require efficient detection processes.

The researchers have worked to tackle these important concerns by using Convolutional Neural Networks (CNN) – hierarchical neural networks highly effective in recognising patterns and structures in data.

“We trained and tested the CNN model on large publicly available ECG datasets featuring subjects with CHF as well as healthy, non-arrhythmic hearts. Our model delivered 100 per cent accuracy: by checking just one heartbeat we are able detect whether or not a person has heart failure,” said study researcher Sebastiano Massaro, Associate Professor at the University of Surrey in the UK.

artificial intelligence, nobel prize
“Artificial intelligence is now one of the fastest-growing areas in all of science and one of the most talked-about topics in society.” VOA

“Our model is also one of the first known to be able to identify the ECG’ s morphological features specifically associated to the severity of the condition,” Massaro said.

Published in Biomedical Signal Processing and Control Journal, the research drastically improves existing CHF detection methods typically focused on heart rate variability that, whilst effective, are time-consuming and prone to errors.

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Conversely, their new model uses a combination of advanced signal processing and machine learning tools on raw ECG signals, delivering 100 per cent accuracy.

“With approximately 26 million people worldwide affected by a form of heart failure, our research presents a major advancement on the current methodology,” said study researcher Leandro Pecchia from the University of Warwick. (IANS)