Evidence map›Paper›PMID 40796609›Full record

ArticleScientific reports2025

An analysis of the real world performance of an artificial intelligence based autism diagnostic.

Carmela Salomon, Kelianne Heinz, Judith Aronson-Ramos, Dennis P Wall

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Review
  5. Article
  6. Article
  7. Observational
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

4 authors.

Carmela SalomonCognoa Inc., 2185 Park Blvd, Palo Alto, CA, 94306, USA. carmela.salomon@cognoa.com.
Kelianne HeinzCognoa Inc., 2185 Park Blvd, Palo Alto, CA, 94306, USA.
Judith Aronson-RamosSociety of Developmental and Behavioral Pediatrics, Virginia, USA.
Dennis P WallCognoa Inc., 2185 Park Blvd, Palo Alto, CA, 94306, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rapidly rising demand for pediatric autism evaluations has outpaced specialist capacity and created a crisis of delayed diagnoses and treatment. Streamlining the diagnostic process could reduce wait times and optimize use of limited specialist resources. Following strong clinical trial results, Canvas Dx, an AI-based diagnostic, was FDA authorized to support accurate diagnosis or rule-out of autism in children 18-72 months with caregiver or healthcare provider concern for developmental delay. To gain insight into real-world device performance, a de-identified aggregate data analysis of the initial 254 Canvas Dx prescriptions fulfilled post-market authorization was conducted to determine: accuracy of autism predictions compared to clinical reference standard diagnosis and prior clinical trial data, key real-world prescriber and patient characteristics, proportion of determinate device outputs (positive or negative for autism) and impact of decision threshold settings on device performance. In this sample of 254 children with a 54.7% autism prevalence rate (29.1% female, average age 39.99 months), Canvas Dx had a NPV of 97.6% (CI- 92.8% -100.0%) and a PPV of 92.4% (CI-87.7%-97.2%). A majority of cases (63.0%) received a determinate result. Sensitivity and specificity of determinate results were 99.1% (CI-97.3%-100.0%) and 81.6% (CI-70.8%-92.5%) respectively. The median age of children who received a positive for autism output was 37.2 months, which is more than 2 years earlier than the current median age of autism diagnosis. No performance differences were noted based on patients' sex. Compared to clinical trial results, real world performance was equivalent for all key metrics, with the exception of the determinate rate and the PPV which were significantly improved in real world performance. Analysis of real-world Canvas Dx data highlights its feasibility and utility in supporting accurate, equitable and early diagnosis or rule out of autism. With medical coverage and broader clinical adoption, innovative solutions such as Canvas Dx can play an important role in helping to address the growing specialist waitlist crisis, ensuring that more children gain access to targeted therapies during the critical window of neurodevelopment where they have the greatest life-changing impact.

Indexed as

Artificial IntelligenceAutism Spectrum DisorderDiagnosis, Computer-AssistedPediatricsAlgorithmsChildChild, PreschoolDevelopmental DisabilitiesFemaleHumansInfantMalePrevalenceSensitivity and SpecificityArtificial intelligenceAutism spectrum disorderDevelopmental delayDiagnosticMachine learningReal-world evidence

Identifiers

PMID40796609
PMCPMC12343959

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.