Martin Styner’s son Max was 6 by the time clinicians diagnosed him with autism. The previous year, Max’s kindergarten teacher had noticed some behavioral signs. For example, the little boy would immerse himself in books so completely that he shut out what was going on around him. But it wasn’t until Max started to ignore his teacher the following year that his parents enlisted the help of a child psychologist to evaluate him.
Max is at the mild end of the spectrum. Even so, Styner, associate professor of psychiatry and computer science at the University of North Carolina at Chapel Hill, wondered if he had been fooling himself by not seeing the signs earlier. After all, Styner has studied autism for much of his career.
Given how complex and varied autism is, it’s not surprising that even experts like Styner don’t always recognize it right away. And even when they do spot the signs, getting an autism diagnosis takes time: Families must sometimes visit the nearest autism clinic for several face-to-face appointments. Not everyone has easy access to these clinics, and people may wait months for an appointment.
That reality has led to a detection gap: Although an accurate diagnosis can be made as early as 2 years of age, the average age of diagnosis in the United States is 4. And yet the earlier the diagnosis, the better the outcome.
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, that 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. Where human intuition and statistical analyses might search for a single, possibly nonexistent trait that consistently differentiates all children with autism from those without, deep-learning algorithms look instead for clusters of differences.
Still, these algorithms depend heavily on human input. To learn new tasks, they ‘train’ on datasets that typically include hundreds or thousands of ‘right’ and ‘wrong’ examples — say, a child smiling or not smiling — manually labeled 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, professor of psychiatry and behavioral 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. Deep learning — and machine learning, more broadly — is not a “magic wand,” says Shrikanth Narayanan, professor of electrical engineering and computer science at the University of Southern California in Los Angeles. When it comes to making a diagnosis and the chance that a computer might err, there are “profound implications,” he says, for children with autism and their families. But he shares the optimism many in the field express that the technique could pull together autism research on genetics, brain imaging and clinical observations. “Across the spectrum,” he says, “the potential is enormous.”