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AI could make detecting autism easier, researchers reveal



FBI resorts to AI to identify 'unreadable prints'

Some researchers say delays in autism diagnosis could shrink with the rise of machine learning—a technology developed as part of artificial-intelligence research.

In particular, they are pinning their hopes on the latest version of machine learning, known as deep learning. “Machine learning was always a part of the field,” Styner says, “but the methods and applications were never strong enough to actually have clinical impact; that changed with the onset of deep learning.”

Deep learning’s power comes from finding subtle patterns, among combinations of features, which might not seem relevant or obvious to the human eye. That means it’s well suited to making sense of autism’s heterogeneous nature, Styner says.

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Still, these algorithms depend heavily on human input. To learn new tasks, they “train” on data sets that typically include hundreds or thousands of “right” and “wrong” examples—say, a child smiling or not smiling—manually labelled by people. Through intensive training, though, deep-learning applications in other fields have eventually matched the accuracy of human experts. In some cases, they have ultimately done better.

“I think these approaches are going to be reliable, quantitative, scalable—and they’re just going to reveal new patterns and information about autism that I think we were just unaware of before,” says Geraldine Dawson, a professor of psychiatry and behavioural sciences at Duke University in Durham, North Carolina. Not only will machine learning help clinicians screen children earlier, she says, but the algorithms might also offer clues about treatments.

Not everyone is bullish on the approach’s promise, however. Many experts note that there are technical and ethical obstacles these tools are unlikely to surmount any time soon.


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