Argus Cognitive STTR Grant Application Abstract Standardized behavioral observation methods are integral to developmental, educational, and behavioral science research. However, existing observational strategies are too laborious to use in large-scale, intervention and dissemination trials needed in autism spectrum disorder (ASD). In addition, current observational strategies do not yield sufficiently quantitative, comparable and granular assessment that could drive the comparison of therapies in clinical trials or the optimization and personalization of intervention. We are developing a minimally intrusive medical device technology (?ARGUS-MDS?) to simultaneously monitor multiple key social and problem behaviors in individuals with ASD and related neurodevelopmental disorders (NDDs). Our team represents an essential collaboration between computer and clinical scientists with expertise in artificial intelligence (AI), NDDs, diagnostics, multi-modal interventions, and psychometrics. We seek support in the form of a Fast Track STTR grant to validate the psychometric properties of ARGUS-MDS and its ability to provide data on change in target behaviors in early childhood and school-aged children. This would then support the development of a scalable, digital treatment progress indicator for behaviors reflecting social, repetitive behavior, and associated symptom profiles in ASD. In Phase I, video and audio data will be collected during gold-standard diagnostic evaluations individuals with ASD (n=15).
Aim 1. 1 will establish quality and clinical validity of ARGUS-MDS algorithms for key social communication behaviors, while Aim 1.2 will evaluate test-retest reliability of biometric output. Phase I will show that ARGUS-MDS meets quality metrics for biometric output, validates the clinician- technician feedback system, and establishes intraclass correlation coefficients for automated social communication (AutoSC) output. In Phase II our focus shifts to establishing psychometric properties of derived scores for AutoSC analysis, evaluating convergence with established clinical and functional measures, and preparing for regulatory filing in Phase III.
Aim 2. 1. will develop scores from biometric data through exploratory and confirmatory factor analyses of social communication behaviors.
Aim 2. 2 evaluates correspondence of AutoSC scores to scores on standardized clinical assessments.
Aim 2. 3 develops a comprehensive Validation Strategy and executes Analytical Validation, per medical device design control regulation and FDA guidance. Phase II will develop scores from AutoSC output, evaluate measurement characteristics of AutoSC scores, reliability & validity of Autos SC scores, and executes all Analytical Validations per the strategy document and FDA guidance. Phase I and II milestones will set us up for commercialization in Phase III, including filing for regulatory approval and product launch. Successful completion of this project will provide a novel, scalable medical device technology to support objective, automated clinical evaluations of social impairments in ASD and other NDDs.
Argus Cognitive STTR Grant Application NARRATIVE We seek to contribute to the digital health movement through validation of a minimally intrusive monitoring system (?ARGUS-MDS?) to simultaneously track multiple key social and problem behaviors in individuals with ASD and related neurodevelopmental disorders (NDDs) including Fragile X Syndrome (FXS). This new technology will make diagnosis and monitoring of these disorders quantitative, scalable, and affordable, removing a serious bottleneck in the current patient journey, related to 1) the limited availability of highly trained human experts and 2) the qualitative and subjective nature of the results attained with current observational strategies. Using advanced video and audio analytics and machine learning, ARGUS-MDS achieves an automated, quantitative, and comprehensive assessment of each patient, across settings (clinical trials, academic research, specialty clinics, pediatric offices and homes), without requiring the presence of a human expert during the monitored diagnostic, monitoring or therapy sessions.
