Tuesday February 19, 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

With Ovarian Cancer Deaths Set to Spike by 67%, AI to Rescue: Study

However, the scans cannot give clinicians detailed insight into patients’ likely overall outcomes or on the likely effect of a therapeutic intervention

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Cancer
Cancer Ribbon. Pixabay

With the incidence of ovarian cancer likely to increase by 55 per cent in another 15 years or so, researchers have created an artificial intelligence (AI) software to help best treat ovarian cancer that will pave the way for personalised medicine and expedite relief, a new study says.

The mathematical software tool — TEXLab — can also predict what treatment might be most effective for patients with the World Ovarian Cancer Coalition predicting that deaths will likely increase by 67 per cent by 2035 due to this particular cancer.

The technology can be used to identify patients who are unlikely to respond to standard treatments and offer alternatives as ovarian cancer is the sixth most common cancer in women in the UK that usually strikes after menopause or those with a family history of the disease.

Early detection of the disease could improve survival rates, the study noted.

“Long-term survival rate for patients with advanced ovarian cancer is poor despite advancements in treatments. There is an urgent need for new ways,” said lead author Eric Aboagye, Professor at Imperial College London.

For the study, researchers used the software to identify the aggressiveness of tumours in CT scans and tissue samples from 364 women with ovarian cancer.

The patients were then given a score known as Radiomic Prognostic Vector (RPV) which indicates how severe the disease is, ranging from mild to severe.

Cancer patient
Cancer patient.

The findings, published in Nature Communications, showed that the software was up to four times more accurate for predicting deaths from ovarian cancer than standard methods.

In addition, five per cent of patients with high RPV scores had a survival rate of less than two years, results showed.

High RPV was also associated with chemotherapy resistance and poor surgical outcomes, suggesting that RPV can be used as a potential bio-marker to predict how patients would respond to treatments.

“Our technology is able to give clinicians more detailed and accurate information on how the patients are likely to respond to different treatments, which could enable them to make better and more targeted treatment decisions,” said Aboagye.

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Doctors as of now diagnose ovarian cancer in a number of ways, including a blood test followed by a CT scan that uses X-rays and a computer to create detailed pictures of the ovarian tumour.

This helps clinicians know how far the disease has spread and determines the type of treatment patients receive, such as surgery and chemotherapy.

However, the scans cannot give clinicians detailed insight into patients’ likely overall outcomes or on the likely effect of a therapeutic intervention. (IANS)