Friday March 23, 2018

Ugandan Engineers Invent Better Way to Diagnose Pneumonia, the leading Killers of Young Children in Africa

Makerere University Telecom graduates Brian Turyabagye (left) and Besufekad Shifferaw (Right) show off the smart jacket that will be used in the diagnosis of pneumonia in children, Kampala, Uganda, April 5, 2017. (H. Althumani/VOA)

ree university engineering graduates in Uganda are taking on one of the leading killers of young children in Africa – pneumonia. They say the prototype of their invention, a “smart jacket” they have named Mama’s Hope, can diagnose the illness faster and more accurately than the current medical protocol.

Four-month-old Nakato Christine writhes on a hospital bed, breathing fast. On the other end of the bed is her twin sister, in the same condition.

Nakato coughs as Senior Nurse Kyebatala Loy adjusts the nasal gastric tube.

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“They have been put on oxygen because they have difficulty in breathing and the feeding is also difficult because of their fast breathing,” Kyebatala said.

Since January, 352 babies have been admitted with pneumonia to pediatric ward 16 at Mulago National Referral Hospital in Kampala.

Pneumonia is the leading infectious cause of death for children under five years of age in Africa and south Asia, according to the World Health Organization. In 2015, pneumonia killed nearly a million children worldwide.

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A key problem is the challenge involved in diagnosing the disease. The sooner the sick children start receiving antibiotics, the better their chance of survival. But health workers armed with stethoscopes and thermometers can miss the infection in its early stage. Dr. Flavia Mpanga of the U.N. Children’s Fund in Kampala says other methods, like the respiratory timer, can lead to misdiagnosis.

“If you see the respiratory timer, it’s got a ticking mechanism that confuses the community health workers. When they are taking the breathe rates, they confuse the ticking sound of the respiratory timer with the breathe rates and every child is almost diagnosed with pneumonia,” said Dr. Mpanga.

She says over-diagnosis means some children are taking antibiotics they don’t need, which is also a public health problem.

A trio of recent university engineering graduates in Uganda think they have an answer. They have been working with the Mulago School of Public Health to test a prototype of their invention, the smart jacket, called Mama’s Hope.

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Two of the inventors, 26-year-old Beseufekad Shifferaw and 25-year-old Brian Turyabagye, gave VOA a demonstration.

“Ahh so…[zipper sound]… the jacket…is placed on the child…first, this goes around the child and then the falcon fastening is placed, and then the flaps are placed…[fade out]”

“This jacket will simply measure the vital signs of pneumonia. That is the breathing rate, the state of the lungs and the temperature,” said Turyabagye. “Now those signs are transmitted to our unit here, through which a health worker can read off the readings, which include cough, chest pains, nausea or difficulty in breathing. With those additional signs and symptoms, they are coupled with the result that has been measured by the jacket and it gives a more accurate diagnosis result.”

For now, it is just a prototype. But the inventors say their tests have shown that the smart jacket can diagnose pneumonia three times faster than traditional exams.

UNICEF has put the team in touch with its office in Copenhagen in charge of innovations to help them advance in the pre-trial stage. Dr. Mpanga sees potential.

“My only hope is that this jacket can reach a commercial value and be regulatory-body approved so that it can help the whole world,” said Dr. Mpanga.

Dr. Mpanga says taking the guess work out of pneumonia diagnosis could save countless lives in the developing world. (VOA)

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This AI Tool May Accelerate Diagnosis Of Eye Diseases, Pneumonia

Besides eye diseases, the tool was able to differentiate between viral and bacterial childhood pneumonia with greater than 90 percent accuracy

The researchers also used occlusion testing, which allowed them to show areas of greatest importance when reviewing the scan images. Pixabay

A novel image-based diagnostic tool, developed using artificial intelligence (AI) and machine learning techniques, may potentially speed up diagnoses and treatment of patients with retinal diseases and pneumonia among children, researchers say.

The findings showed that the new tool uses big data and AI to not only recognize two of the most common retinal diseases — macular degeneration and diabetic macular edema — but also to rate their severity.

“Macular degeneration and diabetic macular edema are the two most common causes of irreversible blindness but are both very treatable if they are caught early,” said Kang Zhang, Professor at the University of California-San Diego.

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“Deciding how and when to treat patients has historically been handled by a small community of specialists who require years of training and are concentrated mostly in urban areas.”

It can also distinguish between bacterial and viral pneumonia in children based on chest x-ray images. IANS

“In contrast, our AI tool can be used anywhere in the world, especially in the rural areas. This is important in places like India, China, and Africa, where there are relatively fewer medical resources,” Zhang said.

For the study, published in the journal Cell, the team studied over 200,000 optical coherence tomography (OCT) images using a technique called transfer learning, where knowledge gained in solving one problem is stored by a computer and applied to different but related problems.

“Machine learning is often like a black box where we don’t know exactly what is happening,” Zhang said.

The researchers then compared the diagnoses from the computer with those from ophthalmologists who reviewed the scans.

ALSO READ: Chronic Diseases Raise Cancer and Mortality Risk

The results showed that the tool “could generate a decision on whether or not the patient should be referred for treatment within 30 seconds and with more than 95 percent accuracy”, Zhang said.

Besides eye diseases, the tool was able to differentiate between viral and bacterial childhood pneumonia with greater than 90 percent accuracy.

It can also discern between cancerous and non-cancerous lesions detected on scans, Zhang said. (IANS)