The system, developed by digital health startup Cognoa, uses information from questionnaires and videos to help pediatricians diagnose autism. It received marketing authorization from the FDA on Wednesday.

In a first, the Food and Drug Administration gave the green light to an algorithm designed to help clinicians diagnose autism in young children. Developed by Palo Alto-based startup Cognoa, the software uses questionnaires from parents, clinicians, and home videos to make a recommendation to assist pediatricians with diagnosis. 

The goal is to identify autism spectrum disorder (ASD) earlier. On average, most kids in the U.S. are diagnosed around age 4. 

“Many of these children are waiting for long periods of time before they get in (to a specialist),” Cognoa CMO Dr. Sharief Taraman, a pediatric neurologist, said in a Zoom interview. “This is a really big deal. We have not had a diagnostic of this kind getting market authorization.” 

Taraman said the software uses machine learning to identify “maximally predictive” features from the questionnaires and two short home videos 

Of course, asking people to provide videos of their kids is very personal. He said families have to give permission for videos to be reviewed by video analysts and the physicians involved in their care.

The FDA’s authorization was based on results from a prospective, double-blinded study that compared how well the software performed in helping diagnose autism compared to a panel of clinicians making a diagnosis based on DSM-5 criteria. Cognoa went through the FDA’s de novo pathway for low- or moderate-risk devices that don’t have a predicate. 

It was evaluated on 425 kids ages 18 months through five years, across 14 different sites. Taraman said the company also made a point to recruit a diverse group of patients for the trial, in terms of race, ethnicity, gender, education and socioeconomic status. Currently, girls and minorities are often diagnosed with ASD at a later age. 

According to the FDA, Cognoa’s test yielded a false positive result in 15 out of 303 kids in the trial without ASD. Meanwhile, it yielded a false negative in just one of the 122 kids with ASD. 

In cases where there wasn’t a clear diagnosis or a rule-out, the algorithm gave an indeterminate result. In total, it provided a diagnosis for about 32% of patients in the trial. 

Having the ability to give an indeterminate result was important, Taraman said, that way the algorithm wouldn’t yield too many false positives, or overlook kids who have other neurodevelopmental conditions that need to be addressed. 

“Technology’s always a tool. It should never be a replacement for a clinician,” he said. “The test is not meant to be a standalone.”

 Cognoa plans to begin marketing the software, called Canvas Dx, later this year. 

“Autism actually is a beautiful thing,” Taraman said. “Our goal is not to ‘turn off’ autism; our goal is to address challenges that come with autism.” 

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Machine learning tools were able to identify biomarkers in blood that could enable earlier diagnosis of children with autism spectrum disorder.

Machine learning tools analyzed hundreds of proteins and identified blood biomarkers that could speed the diagnosis of autism spectrum disorder (ASD), according to a study published in PLOS One.

ASD impacts at least one out of every 59 children in the US, researchers noted. The condition is also associated with significant personal, family, and societal costs. Efforts to determine the underlying biology of ASD, as well as ASD prevention, early diagnosis, and effective treatment, are public health priorities.

Being able to identify children with autism when they’re toddlers could make a big difference, the team stated. Currently, the average age of a child diagnosed with ASD in the US is four years old.

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Diagnosis before the age of four means that a child is more likely to receive an effective, evidence-based treatment, including therapies directed at core ASD symptoms like inflexible behaviors and lack of communication skills.

Researchers have investigated many blood-based biomarker candidates, including neurotransmitters, cytokines, and markers of mitochondrial dysfunction, oxidative stress, and impaired methylation. However, because ASD is so prevalent, using machine learning to incorporate demographic and clinical data into the analysis could more powerfully examine disease status and symptom severity.

For the study, the team examined serum samples from 76 boys with ASD and 78 from typically developing boys aged between 18 months and eight years. The results showed that all nine proteins in the biomarker panel were significantly different in boys with ASD compared with typically developing boys. Researchers found that each of the nine serum proteins correlated with symptom severity.

Researchers evaluated more than 1,100 proteins using the SomaLogic protein analysis platform. The group identified a panel of nine proteins as optimal for predicting ASD using three computational methods. Researchers then evaluated the biomarker panel for quality using machine learning methods.

“The more significantly affected the child is, the higher or lower than normal the blood biomarker is,” said Dwight German, PhD, professor of psychiatry at UT Southwestern and senior author of the study.

“Ideally, there will be a day when a child is identified using blood biomarkers as being at risk for developing ASD and therapies can be started immediately. That would help the child develop skills to optimize their communication and learning.”

The team noted that future studies will need to fully validate the present findings.

“Although the sample size is acceptable for a discovery study, the data presented here are preliminary, and a larger validation study is needed to be certain of the value of the biomarker panel. Due to the increased prevalence of ASD in boys, this study only enrolled boys, which does not allow for an investigation of gender-specific differences,” researchers noted.

The researchers expect that the study will pave the way for earlier diagnosis of autism.

“The earlier we can identify children with autism, the more understanding we can gain on ways to provide support and therapies that will improve their quality of life,” said Laura Hewitson, PhD, at The Johnson Center for Child Health & Development, a multidisciplinary treatment center in Austin, Texas, that uses a unique combination of clinical care, research, and education to further the understanding of ASD and related developmental disorders.

Researchers have previously turned to AI and data analytics techniques to enable earlier autism diagnosis. A study recently published in Nature Medicine showed that a precision medicine method enabled by artificial intelligence could lead to the first biomedical screening tool for a subtype of autism.

“Our study is the first precision medicine approach to overlay an array of research and healthcare data—including genetic mutation data, sexually different gene expression patterns, animal model data, EHR data, and health insurance claims data—and then use an AI-enhanced precision medicine approach to attempt to define one of the world’s most complex inheritable disorders,” said Yuan Luo, associate professor of preventive medicine: health and biomedical informatics at the Northwestern University Feinberg School of Medicine.

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