Later On

A blog written for those whose interests more or less match mine.

AI Detects Autism in Infants (Again)

leave a comment »

Megan Scudellari reports in IEEE Spectrum:

Back in February, we brought you news of a deep-learning algorithm able to predict autism in two-year-olds based on structural brain changes beginning at six months of age.

Now, the same group at the University of North Carolina has again applied machine learning to the goal of predicting autism, with equally impressive results. This time, instead of structural changes, they were able to detect changes in brain function of six-month-olds that predicted if the children would later develop autism.

The study is notable because there were no false positives—that is, all the children predicted to develop autism did.

There were a few misses, however. Of 59 6-month-old infants at high-risk for autism—meaning they had at least one sibling with autism—the algorithm correctly predicted 9 of 11 who later received a positive diagnosis.

By combining this functional analysis with the earlier structural results, it is very possible one could create a highly sensitive and accurate early diagnostic test for autism, says first author Robert Emerson of the Carolina Institute for Developmental Disabilities at UNC. And AI is going to be key to making that happen, he adds.

“It’s going to be really important to use machine learning in the future to pull all these pieces of information together,” says Emerson. In addition to brain scan data, researchers could gather behavioral results, environmental exposures, and more. Once that is done, “we’re going to have a very good shot at really nailing this early prediction.” The paper is published today in the journal Science Translational Medicine.

The team, led by UNC’s Joseph Piven and John Pruett at the Washington University School of Medicine, scanned the brains of infants while they slept. The children were again scanned at age two and completed behavioral and clinical assessments. Each functional connectivity MRI (fcMRI) scan measured the activity of 26,335 brain connections among 230 brain regions.

Using that data, a machine-learning algorithm analyzed how the activity of each piece of the brain was synchronized with other pieces of the brain. The team focused on brain regions associated with key features of autism, such as language skills and repetitive behaviors. . .

Continue reading.

Written by LeisureGuy

11 June 2017 at 3:02 pm

Posted in Medical, Science

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s