Researchers at Aga Khan University and the University of Virginia are collaborating on an innovative project that will harness the power of artificial intelligence to understand a particularly complex disorder of the intestine, environmental enteric dysfunction (EED).
EED – often referred to as a neglected disease of poverty – is widespread among children in low-income countries such as Pakistan where the population is exposed to contaminated water and poor sanitation. EED hinders the gut’s ability to absorb essential nutrients compromising children’s growth potential and leaving them vulnerable to a range of diseases, said a press release issued by Aga Khan University on Tuesday.
Data scientists have already demonstrated how ‘intelligent’ computers can outperform experienced radiologists and pathologists in detecting signs of disease in x-rays and biopsies.
Dr Sana Syed, an assistant professor in paediatrics at the University of Virginia and Dr Asad Ali, associate dean for research at Aga Khan University, are now applying ‘deep learning’, a type of artificial intelligence, to train a computer programme to analyse microscopic images of tissue located deep inside the small intestine.
The initiative, funded through an Engineering in Medicine grant from the University of Virginia (UVa), will be conducted in collaboration with the Data Science Institute at UVa. The project will see computers break down the size, shape and structure of images of the intestine’s cells into a matrix of numbers.
Every number corresponds to a pixel – the smallest unit of an image – and as the programme scans more of these images, it becomes alert to abnormal patterns. Eventually, the computer will learn to compare images of healthy intestines to those affected with EED and to pinpoint the differences at the cellular level that trigger the disorder.
The images of intestines affected by EED being studied come from work in SEEM, a $13 million multi-country grant funded by the Bill and Melinda Gates Foundation.
SEEM is co-led by Dr Asad Ali, associate dean of research at Aga Khan University, and Dr Sean R Moore at the University of Virginia. Along with the images from SEEM, Dr Syed will also be analysing images held in the University of Virginia’s pathology archives as well as those provided by collaborators from the University of Zambia’s School of Medicine.
“Applying cutting-edge data science methods on these images will help us decipher this complex, high-dimensional biomedical data, and yield insights that will improve the way we diagnose the disease,” said Dr Sana Syed, assistant professor in paediatrics at the University of Virginia.
“Advances in computing technology offer a neutral, systematic way to process huge amounts of data and this enables us to pursue a multiomics approach where we analyse information on proteins, chemical compounds and even microorganisms to study all the biological changes caused by EED. This knowledge could then be used to test nutritional or pharmacological interventions that can reduce the harmful health effects of EED.”
In the longer-term, Dr Syed and Dr Ali believe that these insights could also transform the way doctors diagnose EED. At present, the only way to conclusively identify the disease is through a biopsy, an invasive procedure that involves extracting tissue samples from a person’s intestine.
Researchers aim to use the insights from their work to create a comprehensive set of screening biomarkers – chemical warning signs – that would help future clinicians diagnose EED through a simple blood or urine test.
“EED is one of the drivers of chronic public health problems in the developing world such as malnutrition, stunting, and poor response to vaccines,” said Dr Asad Ali. “Addressing EED will help us unsettle the vicious cycle of poverty triggering poor health, and poor health leading to poverty.”
SEEM is a multi-institutional partnership focused on EED. Partners on the project include AKU, the University of Virginia, Cincinnati Children’s Hospital, Massachusetts General Hospital and Washington University.
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