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

Argus认知STTR资助申请 摘要 标准化的行为观察方法是发展，教育和行为不可或缺的。 科学研究然而，现有的观察策略太费力，无法用于大规模干预。 自闭症谱系障碍（ASD）所需的传播试验。此外，目前的观察 战略没有产生足够的定量、可比和细粒度的评估， 临床试验中的治疗比较或干预的优化和个性化。我们 开发微创医疗器械技术（“ARGUS-MDS”）， 监测ASD及相关疾病患者的多种关键社交和问题行为 神经发育障碍（NDD）。我们的团队代表了 计算机和临床科学家，在人工智能（AI），NDD，诊断，多模态 干预和心理测量学。我们寻求快速通道STTR拨款形式的支持来验证 ARGUS-MDS的心理测量特性及其提供早期目标行为变化数据的能力 儿童和学龄儿童。这将支持可扩展的数字治疗的发展 反映ASD中社交、重复行为和相关症状特征的行为进展指标。 在第一阶段，视频和音频数据将在金标准诊断评估过程中收集， ASD（n=15）。目标1.1将确定ARGUS-MDS算法的质量和临床有效性，用于关键社会 交流行为，而目标1.2将评估生物特征输出的重测信度。一期将 显示ARGUS-MDS符合生物特征输出的质量指标，验证临床医生- 技术人员反馈系统，并建立组内相关系数， 社交通信（AutoSC）输出。在第二阶段，我们的重点转移到建立心理测量 AutoSC分析的导出评分属性，评价与已建立的临床和 功能措施，第三阶段准备监管备案。目标2.1。将从 通过探索性和验证性因素分析的社会沟通行为的生物特征数据。目的 2.2评价AutoSC评分与标准化临床评估评分的一致性。目标2.3 根据医疗器械设计，制定全面的确认策略并执行分析确认 控制法规和FDA指南。第二阶段将根据AutoSC输出结果制定评分， AutoSC评分的测量特征，AutoSC评分的信度和效度，以及 根据战略文件和FDA指南执行所有分析确认。I期和II期 里程碑将为我们在第三阶段的商业化奠定基础，包括提交监管批准， 产品发布。该项目的成功完成将提供一种新颖的、可扩展的医疗器械技术 支持对ASD和其他NDD中的社会障碍进行客观、自动化的临床评估。

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.