Now, there is a potential for machine learning algorithms applied to biopsy images to shorten the time for diagnosing and treating a gut disease that often causes permanent physical and cognitive damage in children from impoverished areas. According to a study published in the open access journal JAMA Open Network by scientists from the University of Virginia Schools of Engineering and Medicine and the Data Science Institute.
In places where sanitation, potable water, and food are scarce, there are high rates of children suffering from environmental enteric dysfunction, a disease that limits the gut's ability to absorb essential nutrients and can lead to stunted growth, impaired brain development, and even death.
Even when the disease affects some children in rural Virginia, it also affects 20 percent of children under the age of 5 in low-and-middle-income countries, including Bangladesh, Zambia, and Pakistan.
According to an assistant professor of pediatrics in the UVA School of Medicine, Dr. Sana Syed, this research is the reason she got into medicine. She noted that talking about a disease that affects hundreds of thousands of children, and that is entirely preventable.
The founding director of the UVA Data Science Institute, Donald Brown, and W.S. Calcott, Professor in the Department of Engineering Systems and Environment, are the colleagues Syed is working with to incorporate machine learning into the diagnostic process for health officials combating this disease.
An in-depth learning approach, Syed and Brown are using is called "convolutional neural network" to train computers to read thousands of images of biopsies. Then, pathologists can learn from the algorithms on how to more effectively screen patients based on where the neural network is looking for differences and where it is focusing its analysis on getting results.
Brown acknowledged that these are the same types of algorithms Google is using in facial recognition, but they are using them to aid in the diagnosis of disease through biopsy images.
Offering insights that have evaded human eyes, the machine learning algorithm can validate pathologists' diagnoses and shorten the time between imaging and diagnosis, and from a technical engineering perspective, might be able to offer a look into data science's "black boxes" by giving clues into the thinking mechanism of the machine.
However, this situation is still about saving lives as Syed sees it.
She explained that there are so much poverty and such an unfair set of consequences. It scientists can use these cutting-edge technologies and ways of looking at data through data science, they can get answers faster and help these children sooner.
