Integration of an Artificial Intelligence–Based Autism Diagnostic Device into the ECHO Autism Primary Care Workflow: Prospective Observational Study
Bibliographic record
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 autistic children 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 FDA authorized for use, in conjunction with clinical judgement, in 18-72-month-olds with indicators of developmental delay. OBJECTIVE: 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 18-72-months-olds 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 was 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 (IRB assigned project number 2075722). RESULTS: 80 children and seven clinicians completed the study. On average, time from clinical concern at study enrollment to final autism diagnosis was 39.22 days, compared to 180-264 day waits at adjacent specialist referral centers. The vast majority (93%) 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 52.50% of participants, and in all cases these were consistent with the final clinical determination. Participating clinicians reported 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 DSM-5 criteria when formulating the diagnosis for indeterminate cases. CLINICALTRIAL: Registered with ClinicalTrials.gov (Protocol Identifier: NCT05223374). INTERNATIONAL REGISTERED REPORT: RR2-10.2196/37576.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".