Autism Will Soon Be Able To Be Detected In Toddlers Using A Smartphone
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Autism Will Soon Be Able To Be Detected In Toddlers Using A Smartphone 5/14/21, 10:13 AM
May 14, 2021, 01:12pm EDT
Autism Will Soon Be Able To Be
Detected In Toddlers Using A
Smartphone
Gabriel A. Silva Contributor
I write about neuroscience and its intersection with technology.
Diagnosing Autism early is critical to success later in life. GETTY
Identifying Autism in toddlers is critical to beginning interventions early,
and is associated with improved outcomes later in life. Soon, pediatricians
and other health care providers will be able to install an app on their
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Autism Will Soon Be Able To Be Detected In Toddlers Using A Smartphone 5/14/21, 10:13 AM
smartphone or tablet that is capable of analyzing the visual gaze of a
toddler in order to determine if they may be on the Autism spectrum.
Eventually, parents and others will be able to download it onto their own
mobile devices and do the screening themselves. This new research out of
Duke University has the potential to expand the reach of early screening,
and therefore get possibly affected children in for detailed clinical
evaluations faster.
Autism spectrum disorder (ASD) is a set neurodevelopment conditions
characterized by a broad range of challenges associated with social and
communication skills, varying degrees of repetitive and stereotyped
behaviors, and different types of sensitivities to the environment. Autism
is common. As of 2016, it is estimated that about 1 in 54 children were
affected.
In a laboratory setting, Autism can be detected in children as young as six
months by observing visual responses to social cues. It turns out that how
young children visually engage with others in social settings affects how
brain circuits responsible for social interactions develop. When these
circuits don’t develop as they should, it can lead to increased challenges
for successful social engagement and communication later on. By
identifying potential Autism early, interventions aimed at facilitating
social interactions can begin to mitigate the effects of reduced social
attention and the consequences it has on the development of the child.
Dr. Geraldine Dawson, one of the paper’s senior authors and the William
Cleland Distinguished Professor of Psychiatry and Behavioral Sciences,
and Director of the Duke Institute for Brain Sciences and the Duke Center
for Autism and Brain Development, emphasized three major issues
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addressed by this work. First, the traditional questionnaires that are used
to collect data to screen for Autism are prone to false positive results. In
fact, about 50% of toddlers that the questionnaires suggest may have
Autism will actually not have it. Second, there are typically long waits in
order to gain access to clinical experts capable of fully evaluating a child in
order to make definite diagnosis. And in many parts of the country that
access is limited or unavailable. To make matters worse, because of the
false positive rate of the traditional screening methods, many parents are
putting children on waitlists to be tested when in fact they don’t need to
be, further adding to the backlog. On the flip side, because the
questionnaires tend to be long and cumbersome some pediatricians don’t
screen at all, potentially delaying access to treatments for affected
children.
The third reason is an important issue related to equity of care. As Prof.
Dawson explained, “The questionnaire does not perform well with
families of color or families with lower educational backgrounds. So the
challenge for us was how do you develop a tool that directly observes the
child’s behavior, and assess whether the child is displaying Autism
symptoms?”
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In effect, the app the Duke team is developing takes what has until now
been a qualitative and subjective screening assessment with a significant
amount of inherent error, to a much more accurate quantitative objective
evaluation that will relatively soon be able to be used by any family doctor
or pediatrician. This is particularly valuable in, say, a rural setting away
from a large medical center where access to specialists may be limited.
The specialized equipment and expertise necessary to measure visual gaze
in children would normally be limited to a clinical laboratory that is not
widely available or feasible. In fact, the scientists’ had these constraints in
mind from the beginning, working with clinicians on the front line to
make sure the research is applicable to real world scenarios, not just
controlled lab environments.
How Engineering is Transforming Diagnosis
In a broader context, this work is just one example of the transformational
contributions engineering tools are beginning to have on the study of
behavior and on neurodevelopmental and neuropsychiatric disorders. Up
until recently, behavioral work has relied almost exclusively on subjective
observations by other humans. A trained expert observes the child and
subjectively scores them on a numerical scale as they perform different
motor, social, and cognitive tasks.
To be sure, such expert-intensive observational screening continues to be
the cornerstone that provides patients and their families validated clinical
diagnoses and access to therapeutic resources. But there exists the
opportunity to do much better. There is a wide gap between the
observational and subjective reporting methods of behavioral evaluations
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and the quantitative methods and tools that engineering and related fields
can bring to bear. Technology has an important role to play here.
In the case of the Duke team’s work, Prof. Dawson joined forces with Prof.
Guillermo Sapiro, the James B. Duke Distinguished Professor of Electrical
and Computer Engineering, and Professor of Computer Science. Prof.
Sapiro is an expert in computer vision, computer graphics, medical
imaging, and machine learning. All this heavy duty engineering is what
enabled the development and testing of the algorithms and tools that
make up the app.
It is what Prof. Dawson calls ‘quantitative phenotyping’. A phenotype is a
set of observable and measurable characteristics and traits in an
individual that result from complex interactions between genetic makeup
and the environment.
For example, in the current study the researchers measured visual gaze in
response to toddlers watching specially constructed video clips on an
iPhone or iPad. But in related work, they used the same app to measure
the time it took Autistic children to turn their heads when their name was
called out. They were able to do so with much greater sensitivity and
resolution than human observers. This simple test is a valuable clinical
indicator since the reaction time a child takes to turn towards someone
calling them is correlated with other behavioral and communication
challenges linked to Autism.
Eventually, the goal is not to measure a single feature associated with a
potential diagnosis of Autism, but to integrate multiple features - such as
the time it takes a child to turn their head when their name is called in
addition to visual gaze analysis. Such tools will offer potentially powerful
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diagnostic and predictive methods that don’t exist today. As Prof. Dawson
put it: “If you combine gaze to social versus non-social information to gaze
patterns in a conversation, your ability to accurately predict Autism
increases. Imagine doing that with multiple more features that can be
derived from computer vision.”
Importantly, these kinds of analyses tools have a critical role to play not
just for evaluating an individual’s state in the present, but also for tracking
changes and progress (or regression) in a patient over time. Tracking
trends in measured variables is of tremendous clinical value. It can
uncover changes due not just to the progression of Autism (or other
neurodevelopmental or psychiatric disorders), but also to factors such as
how age, or learning, or therapeutic approaches are affecting and
interacting with the evolving symptoms and disorder.
Still, Prof. Dawson thinks that it will likely be a few years before the work
is sufficiently validated and ready for widespread distribution among
health care providers, and even longer before parents and others will be
able to download the app to their smartphones or tablets. The team’s
cautious approach is not without merit. Part of the reason it will take so
long is to ensure that the algorithms and analyses methods they use
properly capture potential considerations across different cultural and
socioeconomic variables.
The wait will be worth it though. As engineering methods, tools, and ways
of thinking further contribute to neuroscience, how Autism and other
neurodevelopmental and psychiatric disorders are defined, diagnosed,
and eventually treated will likely be very different than they are today.
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Gabriel A. Silva
I am a Professor of Bioengineering and Neurosciences at the University of California
San Diego, where I hold a Jacobs Family Scholar in Engineering Endowed Chair. I have…
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