A toddler plays
a bubble-popping game as part of a 10-minute tablet app that can greatly aid in
screening children for autism. Credit: Duke University
Researchers at
Duke University have demonstrated an app driven by AI that can run on a tablet
to accurately screen for autism in children by measuring and weighing a variety
of distinct behavioral indicators.
Called SenseToKnow, the app delivers scores that
evaluate the quality of the data analyzed, the confidence of its results and
the probability that the child tested is on the autism spectrum.
The results are fully interpretable, meaning that they spell out exactly which
of the behavioral indicators led to its conclusions and why.
This ability gives health care providers detailed
information on what to look for and consider in children referred for full
assessments and intervention.
SenseToKnow's ease of use and lack of hardware
limitations, combined with its demonstrated accuracy across sex, ethnicity and
race, could help eliminate known disparities in early autism diagnosis and
intervention by allowing autism screening to take place in any setting, even in
the child's own home. The results appear in the journal Nature Medicine.
"Autism is characterized by many different behaviors, and not all children on the spectrum display all of them equally, or at all," said Geraldine Dawson, director of the Duke Center for Autism and Brain Development, who is a co-senior author on the study. "This screening tool captures a wide range of behaviors that more accurately reflect the complexity and variability found in autism."
A toddler plays a bubble-popping game as part of
a 10-minute tablet app that can greatly aid in screening children for autism. Credit:
Duke University
Recent research has shown promising results from tracking children's eye
movements in response to specially designed movies that can help diagnose
autism in a clinical setting. SenseToKnow, the researchers say, detects a wider
range of behaviors such as facial expression, gaze patterns, head movements and
blink rate. It also incorporates an on-screen bubble-popping game to assess
motor movement and skills, as delays in motor skills are one of the earliest
signs of autism.
The app uses almost every sensor in the tablet's arsenal to measure and
characterize the child's response without the need for any sort of calibration
or special equipment. It then uses AI to analyze the child's responses to
predict how likely it is that the child will be diagnosed with autism.
"The AI we've built compares each child's biomarkers to how indicative
they are of autism at a population level," said Sam Perochon, a Ph.D.
student working in the laboratory of Guillermo Sapiro, the James B. Duke
Distinguished Professor of Electrical and Computer Engineering and co-senior
author of the study. "This allows the tool to capture behaviors
other screening tests might
miss and also report on which biomarkers were of the most interest and most
predictive for that particular child."
The AI tool is able to provide scores for both the quality of data that the
app was able to capture as well as its level of confidence in its own
analysis—both of which, the researchers believe, are a novel feature.
Snippets of videos and games designed to elicit
specific responses in children while the tablet they are playing on records
their behaviors. The SenseToKnow app then uses AI to analyze their responses
and compare it to a baseline created from a large database to detect complex
indications of autism. Credit: Duke University
"This is an important aspect for a health care provider to know,
just like they would need to know if a blood test did not
have a big enough sample to produce reliable results," said Matias Di
Martino, assistant research professor of electrical and computer engineering at
Duke, who co-led the analysis of the study with Sapiro and Perochon.
In the study, SenseToKnow was administered to 475 children during a
pediatric well-child visit, 49 of whom were subsequently diagnosed with autism
and 98 with developmental delay without autism. The app showed 87.8%
sensitivity for detecting autism, meaning it correctly identified most children
with the condition. Its specificity—the percentage of children without autism
who screened negative—was 80.8%.
Overall, participants who screened positive for autism using the app had a
40.6% probability of subsequently being diagnosed with the condition. In comparison,
only about 15% of children who screen positive using the standard parent
questionnaire are later diagnosed with autism. Combining the app with the
standard questionnaire boosted the probability of a positive screen resulting
in later diagnosis to 63.4%—meaning fewer children are falling through the
cracks.
The American Academy of Pediatrics recommends that every toddler be
screened for autism at 18 and 24 months. However, concerns have been raised
that current screening methods that rely solely on parent reporting are missing
children. Girls and children of color, in particular, are often missed.
A toddler
watches a movie on a tablet while the tablet tracks characteristics such as eye
movement, gaze and head movement to screen for autism. Credit: Duke University
SenseToKnow's
ability to detect autism was similar across children of different sexes, races and ethnicities. While the researchers
do not envision the digital screening tool replacing parent reporting, they
believe it is important to augment the subjective questionnaire with objective
tools to help close the gap.
"Just like when any patient goes to their doctor,
the doctor listens to them describe what they are experiencing, but they also
use thermometers and other objective tests to provide additional information to
guide next steps and referrals for further evaluation," Dawson said.
"Such objective tests have been missing for autism."
The researchers are currently conducting a study in
which parents deliver the app at home. They hope that the app will also be
useful for measuring a child's progress within an early intervention program as
well as to studying the effectiveness of such programs.
"There is a wide range of expertise among health care providers in knowing and being able to recognize all the potential signs of a child being on the autism spectrum," Dawson said. "This app could help clinicians focus on the areas in which the child needs help, as well as identify areas of strength."
Source: Tablet-based AI app measures multiple behavioral indicators to screen for autism (medicalxpress.com)
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