Standard Home Video Becomes an Early Diagnostic Tool for Infant Development
The Client:
A healthcare technology company working with clinical partners to address the challenge of late detection of neurodevelopmental conditions in infants particularly autism where early intervention is critical but access to clinical assessment is limited.
The Engagement:
SSI developed an AI-powered infant monitoring system that analyzes standard home video footage to detect developmental milestones and motor pattern anomalies associated with neurodevelopmental conditions.
The Challenge:
Autism and related neurodevelopmental conditions are historically diagnosed based on parental observation and subjective clinical assessments: a system that is slow, inconsistent, and highly dependent on the clinician’s experience.
Every child develops differently, making evidence-based threshold tracking difficult without large volumes of labeled training data. The ‘small data’ problem was a significant barrier to building reliable ML models.
Existing diagnostic tools required specialized equipment or clinic visits putting early detection out of reach for families in underserved areas or those without immediate access to pediatric specialists.
The Solution:
SSI built the system on a purpose-trained foundational model, fine-tuned to analyze video captured on standard home cameras no specialized hardware required.
The AI interprets subtle behavioral and motor patterns: posture, movement timing, motor milestone achievement by age group signals that are difficult for a non-specialist observer to systematically track.
To overcome the small-data challenge, SSI applied synthetic data augmentation and domain-informed learning expanding the effective training set and improving model generalization across the natural variation in infant development.
The Impact:
The system gave clinicians and parents an objective, video-based developmental tracking tool moving the first line of detection from the clinic waiting room to the home environment.
Age-group milestone tracking provided a structured, consistent framework that reduced the subjectivity of diagnosis and created a documented developmental record for clinical handoff.
