Evidence map›Paper›PMID 41124399›Full record

Observational studyJMIR formative research2025

Integration of an Artificial Intelligence-Based Autism Diagnostic Device into the ECHO Autism Primary Care Workflow: Prospective Observational Study.

Kristin Sohl, Erik Linstead, Kelianne Heinz, Elia Eiroa Lledo, Alicia Brewer Curran, Melissa Mahurin, Valeria Nanclares-Nogués, Carmela Salomon, Minda Seal, Sharief Taraman

Abstract readObservational Study
In one paragraph

Observational study in JMIR formative research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Kristin SohlECHO Autism Communities, University of Missouri School of Medicine, Columbia, MO, United States.ORCID 0000-0003-0588-8742
Erik LinsteadFowler School of Engineering, Chapman University, Orange, CA, United States.ORCID 0000-0003-0174-7002
Kelianne HeinzCognoa Inc, 5000 Campus Drive, Newport Beach, CA, 92663, United States, 1 6507852624.ORCID 0009-0004-7027-9745
Elia Eiroa LledoFowler School of Engineering, Chapman University, Orange, CA, United States.ORCID 0000-0001-5232-0864
Alicia Brewer CurranECHO Autism Communities, University of Missouri School of Medicine, Columbia, MO, United States.ORCID 0000-0002-9032-1953
Melissa MahurinECHO Autism Communities, University of Missouri School of Medicine, Columbia, MO, United States.ORCID 0000-0002-7284-1082
Valeria Nanclares-NoguésECHO Autism Communities, University of Missouri School of Medicine, Columbia, MO, United States.ORCID 0000-0001-7559-8180
Carmela SalomonCognoa Inc, 5000 Campus Drive, Newport Beach, CA, 92663, United States, 1 6507852624.ORCID 0000-0003-2662-4308
Minda SealCognoa Inc, 5000 Campus Drive, Newport Beach, CA, 92663, United States, 1 6507852624.ORCID 0009-0004-7712-4156
Sharief TaramanCognoa Inc, 5000 Campus Drive, Newport Beach, CA, 92663, United States, 1 6507852624.ORCID 0000-0003-2108-2490

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Pediatric specialist shortages and rapidly rising autism prevalence rates have compelled primary care clinicians to consider playing a greater role in the autism diagnostic process. The ECHO Autism: Early Diagnosis Program (EDx) prepares clinicians to screen, evaluate, differentiate, diagnose, and provide longitudinal care for children with autism in primary care settings. Canvas Dx is a prescription-only Software as a Medical Device designed to support clinical diagnosis or rule out of autism, including in primary care settings. It is authorized by the Food and Drug Administration for use, in conjunction with clinical judgment, in 18- to 72-month-olds with indicators of developmental delay. Objective: This study aims to assess the feasibility and impact of integrating the device into the ECHO Autism: EDx workflow. Time from the first clinical question of developmental delay to autism diagnosis is the primary endpoint. Secondary endpoints explore clinician and caregiver experience of device use. Methods: Children aged 18 to 72 months with concern for developmental delay indicated by either a caregiver or health professionals were eligible to participate in this prospective observational study. Experienced ECHO Autism: EDx clinicians were recruited to evaluate the inclusion of the device as part of their diagnostic evaluations. Outcome data were collected via a combination of electronic questionnaires, standard clinical care record reviews, and analysis of device outputs. Institutional review board approval was provided by the University of Missouri-Columbia (project number 2075722). Results: Eighty children and 7 clinicians completed the study. On average, time from clinical concern at study enrollment to final autism diagnosis was 39.22 days, compared to 180- to 264-day waits at adjacent specialist referral centers. The vast majority (93%, 50/54) of caregivers reported being satisfied with the ECHO Autism: EDx plus device evaluation their child received and endorsed that they would recommend it to others and that they felt comfortable using the device. The device produced determinate autism predictions or rule-outs for 52.5% of participants, and in all cases, these were consistent with the final clinical determination. Participating clinicians reported that device use was feasible and reduced several challenges associated with their previous diagnostic process; however, they noted it did not obviate the need for additional structured observation in every case. Conclusions: The ECHO Autism: EDx plus device workflow offers considerable time savings compared to specialty center referral and was strongly endorsed by caregiver participants. Embedding the device into the ECHO Autism: EDx workflow was feasible and helped streamline several workflow efficiencies. Clinicians still utilized their training and application and interpretation of Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, criteria when formulating the diagnosis for indeterminate cases.

Indexed as

Artificial IntelligenceAutistic DisorderWorkflowChildChild, PreschoolEarly DiagnosisFemaleHumansInfantMalePrimary Health CareProspective Studiesartificial intelligenceautism spectrum disorderdevelopmental disabilitiesdiagnosis, computer-assistedearly diagnosismachine learningpediatric primary careprimary health carereal-world evidence

Identifiers

PMID41124399
PMCPMC12543035

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.