Getting creative with artificial intelligence
CAMBRIDGE, Massachusetts: MIT students are getting creative with artificial intelligence (AI).
Their creativity ranges from using it to screen for autism, to mapping the spread of Ebola virus and preventing maternal deaths in Rwanda.
In collaboration with Boston Children’s Hospital and Harvard Medical School, MIT researchers are using AI to explore autism’s hidden origins.
Working with his advisors, Bonnie Berger and Po-Ru Loh, professors of maths and medicine at MIT and Harvard respectively, graduate student Maxwell Sherman has helped develop an algorithm to detect previously unidentified mutations in people with autism which cause some cells to carry too much or too little DNA.
The team has found that up to one per cent of people with autism carry the mutations, and that inexpensive consumer genetic tests can detect them with a mere saliva sample. Hundreds of US children who carry the mutations and are at risk for autism could be identified this way each year, researchers say.
“Early detection of autism gives kids earlier access to supportive services,” says Sherman, “and that can have lasting benefits.”
AI is also tracking the spread of
Sierra Leone had one doctor for every 30,000 residents, and the doctors were the first to be infected. That further reduced the availability of medical help. – Marie Charpignon, graduate student
Ebola.
By the time the Ebola virus spread from Guinea and Liberia to Sierra Leone in 2014, the government was prepared. It quickly closed its schools and shut its borders with the two countries. Still, relative to its population, Sierra Leone fared worse than its neighbours, with 14,000 suspected infections and 4,000 deaths.
Marie Charpignon, a graduate student in the MIT Institute for Data, Systems, and Society (IDSS), wanted to know why.
In a network analysis of trade, migration, and World Health Organization data, Charpignon discovered that a severe shortage of medical resources seemed to explain why Ebola had caused relatively more devastation in Sierra Leone, despite the country’s precautions.
“Sierra Leone had one doctor for every 30,000 residents, and the doctors were the first to be infected,” she says.
“That further reduced the availability of medical help.”
If Sierra Leone had not acted as decisively, she says, the outbreak could have been far worse. Her results suggest that epidemiology models should factor in where hospitals and medical staff are clustered to better predict how an epidemic will unfold.
AI is also being used to prevent maternal deaths.
The top cause of death for new mothers in Rwanda are infections following a caesarean section.
To identify at-risk mothers sooner, researchers at MIT, Harvard Medical School, Brigham Women’s Hospital, and Partners in Health, Rwanda, are developing a computational tool to predict whether a mother’s post-surgical wound is likely to be infected.
Researchers gathered Csection wound photos from 527 women, using health workers who captured the pictures with their smartphones 10 to 12 days after surgery.
Working with his advisor, Richard Fletcher, a researcher in MIT’s D-Lab, graduate student Subby Olubeko helped train a pair of models to pick out the wounds that developed into infections. When they tested the logistic regression model on the full dataset, it gave almost perfect predictions.
The colour of the wound’s drainage, and how bright the wound appears at its centre, are two of the features the model picks up on, says Olubeko. The team plans to run a field experiment this spring to collect wound photos from a more diverse group of women and to shoot infrared images to see if they reveal additional information. — MIT News