ASD Prediction Using Infant Brain Structural MRI Analysis
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Solution Overview
Problem
Current methods for diagnosing autism spectrum disorder (ASD) are limited by late detection, relying on subjective behavioral evaluations that typically occur after 24 months, and lack effective early biomarkers for intervention.
Innovation Solution
A system and method utilizing brain structural characteristics from magnetic resonance imaging (MRI) data to predict ASD diagnosis by analyzing cortical surface area and thickness measurements in infants, employing a deep learning algorithm to identify patterns associated with ASD development before behavioral symptoms emerge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If observational/behavioral criteria are used for autism diagnosis, then diagnosis can be made, but detection occurs late (24 months or later) and depends on subjective clinical evaluation
Solution Approach 1:
The patent applies preliminary action by using brain imaging data from infants before 24 months of age to predict future ASD diagnosis. The system analyzes brain structural characteristics (cortical surface area, cortical thickness, brain volume) at 6-12 months to identify children who will develop ASD by 24 months, enabling early detection before behavioral symptoms fully emerge.
Solution Approach 2:
The patent uses brain structural characteristics as an intermediary biomarker to bridge the gap between early brain development and later behavioral symptoms. Instead of directly observing behavior at 24 months, the system measures brain structure at 6-12 months as an intermediate indicator that predicts future diagnostic status.
2Loss of time
If brain imaging data is analyzed to predict ASD diagnosis before 24 months, then early detection is achieved, but the system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the brain imaging analysis into distinct measurable components: cortical surface area, cortical thickness, and brain volume. Each of these structural characteristics is analyzed separately using specialized algorithms, making the complex imaging data manageable and interpretable while maintaining predictive accuracy.
Solution Approach 2:
The patent transforms complex brain imaging data into quantifiable structural parameters (surface area, thickness, volume) that can be systematically measured and compared. By converting imaging data into these standardized parameters, the system reduces complexity while preserving the essential information needed for prediction.
3Reliability
If subjective behavioral evaluation is used for diagnosis, then diagnosis can be made, but the evaluation is based on behaviors that begin to emerge around 24 months
Solution Approach 1:
The system performs preliminary assessment of brain structure before behavioral symptoms emerge. By measuring brain structural characteristics at 6-12 months, the system identifies children at risk for ASD before the 24-month mark when behavioral evaluation becomes reliable, thus gaining time for early intervention.
Data Source
AI summary
Methods, systems, and computer readable media for utilizing brain structural characteristics for predicting a diagnosis of a neurobehavioral disorder are disclosed. One method for utilizing brain structural characteristics for predicting a diagnosis of a neurobehavioral disorder includes receiving brain imaging data for a human subject of at least one first age. The method also includes determining, from the brain imaging data, measurements of brain structural characteristics of the human subject and inputting the brain structural characteristics into a model that predicts, using the measurements of the brain structural characteristics, a diagnosis of a neurobehavioral disorder at a second age greater than the at least one first age.