ARGUS-MDS: automated, quantitative and scalable system for social processes in behavioral health

ARGUS-MDS：行为健康社会过程的自动化、定量和可扩展系统

• 批准号: 10019734
• 负责人: Attila Meretei
• 金额: $ 48.51万
• 依托单位: ARGUS COGNITIVE, INC.
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-06 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10019734
• 关键词: Address; Algorithms; Applications Grants; Area; Artificial Intelligence; Automation; Behavior; Behavioral Sciences; Biometry; Characteristics; Child; Childhood; Clinic; Clinical; Clinical Sciences; Clinical Trials; Clinical assessments; Cognitive; Collaborations; Computer Vision Systems; Computer software; Computers; DSM-V; Data; Development; Diagnosis; Diagnostic; Disease; Evaluation; Evidence based practice; Face; Facial Expression; Factor Analysis; Feedback; Fragile X Syndrome; Gestures; Goals; Gold; Grant; Guidelines; Healthcare; Home; Human; Impairment; Individual; Intervention Trial; Language; Length; Machine Learning; Measurement; Measures; Medical Device; Medical Device Designs; Methods; Modeling; Monitor; Movement; Nature; Neurodevelopmental Disorder; Nonverbal Communication; Outcome; Outcome Measure; Output; Patients; Phase; Population; Preparation; Problem behavior; Process; Property; Psychometrics; Public Health; Regulation; Reporting; Research; Sampling; School-Age Population; Scientist; Small Business Technology Transfer Research; Social Behavior; Social Processes; Speech; Standardization; System; Technology; Testing; Therapeutic; Training; Validation; Validity and Reliability; analog; associated symptom; autism spectrum disorder; autistic children; behavior observation; behavioral health; clinical practice; commercialization; communication behavior; digital health; digital treatment; disability; dissemination trial; early childhood; experimental study; functional disability; functional outcomes; gaze; improved; individuals with autism spectrum disorder; machine learning algorithm; medical specialties; motor control; multi-component intervention; neuropsychiatry; new technology; novel; personalized intervention; programs; repetitive behavior; research clinical testing; screening; showing emotion; social; social communication; social deficits; social reciprocity

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.

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