Screening Support, Not Diagnosis
Human experts remain in control. ASDFACE provides structured input for better-informed next steps.
AI STARTUP FOR EARLY SCREENING IMPACT
One face image can provide a strong autism screening indication in minutes.
In Australia, autism can be diagnosed from around 18 months, yet the average diagnosis age is around 8 years. ASDFACE bridges this gap with an explainable computer-vision platform designed for clinicians, families, and social care systems.
- 18 months Earliest clinical diagnosis window
- 8 years Average diagnosis age in Australia
- 5 min Screening workflow goal
- NDIS Designed to reduce pathway pressure
SUMMARY
Bridging the delay between detectability and diagnosis.
ASDFACE is an AI computer-vision platform and app that uses facial images to support autism screening. It is designed to streamline triage and referral, not replace clinicians or formal diagnosis.
By accelerating early risk identification, we aim to reduce pressure on long specialist waitlists and on disability support pathways, including families seeking NDIS-relevant services.
Designed for Families and Professionals
Workflow supports clinicians, educators, support workers, and parents across real-world service pathways.
From Local Need to Global Use
Built in Australia with architecture that can adapt across policy, language, and healthcare settings.
PLATFORM WORKFLOW
From a single image to referral-ready screening insights.
Image Capture
Guided intake supports quality facial-image data capture through a simple app experience.
Domain-Adapted AI Analysis
AI-enhanced facial biometrics analyze subtle cues while adapting across cameras and environments.
Explainable Risk Indication
Screening outputs are presented with interpretable confidence to support professional judgement.
Referral and Follow-Up
Structured reports help teams move families toward timely formal assessment and intervention planning.
CONTRIBUTION TO AI KNOWLEDGE
Research translated into accessible clinical-facing tools.
ASDFace + Domain Adaptation
ASDFace applies AI-enhanced facial biometrics to detect subtle markers often difficult to identify by eye. Domain adaptation improves reliability across different capture devices.
DeepMNF Neuroimaging Framework
The Deep Multimodal Neuroimaging Framework (DeepMNF) combines MRI modalities to identify autism-related neural signatures and has reported 95.21% AUC performance in child cohorts.
SCAI Educational Ecosystem
StellarCare AI (SCAI) is trained on 30,000+ autism publications to provide around-the-clock guidance and personalized rehabilitation planning support, while improving practical AI literacy.
Reported figures above are based on ASDFACE research outputs and collaboration materials.
COLLABORATION & PARTNERSHIPS
Built through university, industry, and public-support ecosystems.
NDIS Pathway Relevance
ASDFACE is designed to support faster referral pathways and reduce pressure on long disability and specialist support queues.
Visit NDIS →Support from OTARC
We acknowledge support from La Trobe University's Olga Tennison Autism Research Centre (OTARC) for translational autism research.
Visit OTARC →Support from ACAMI
We acknowledge support from La Trobe University's Australian Centre for Artificial Intelligence in Medical Innovation (ACAMI).
Visit ACAMI →Global Collaboration
Collaboration has included La Trobe University, Harvard Medical School, IBM, and the CSIRO iPhD project (2025-2029) connecting research with commercialization.
Read IBM Case Study →SCALABILITY, REACH, SUSTAINABILITY
Commercially scalable while focused on clinical responsibility.
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Fast screening workflow target
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Specialist waitlist pressure addressed
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Autism publications powering SCAI
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Reported DeepMNF child-cohort performance
Commercial Pathway
Multi-tier models for government agencies, practitioners, and families are designed for long-term sustainability and broad access.
Awards and Momentum
Program materials report recognition including the 2024 Victoria Innovation Award and new research funding in 2025.
Ethics and Privacy
Mobile-first on-device processing helps avoid sending sensitive images to servers, and governance focuses on fairness, safety, and transparent AI use, including collaboration with Autism Australia on protocol quality.
PARTNERSHIP ENQUIRIES
Build high-impact pilots with ASDFACE across care and community systems.
We welcome collaboration with hospitals, disability organizations, universities, and policy-aligned technology partners.
SOCIAL BENEFIT FOCUS
Technology outcomes measured by real family and system impact.
Families
Faster screening signals can reduce uncertainty and help families access evidence-based support pathways earlier.
Clinicians and Educators
Structured, explainable outputs strengthen triage decisions and improve coordination across multidisciplinary teams.
Public Systems
Earlier identification supports fairer resource allocation and can reduce pressure on delayed specialist and disability pathways.