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

Argus Cognitive STTR奖助金申请 摘要 标准化的行为观察方法对于发展、教育和行为是不可或缺的 科学研究。然而，现有的观测策略太费力，不能用于大规模的干预。 以及自闭症谱系障碍(ASD)所需的传播试验。此外，目前的观测数据 战略没有产生足够的量化、可比性和细粒度的评估，可能会推动 临床试验中的治疗方法比较或干预措施的最优化和个性化。我们是 开发一种侵入性最小的医疗设备技术(“Argus-MDS”)以同时 监测ASD及相关个体的多个关键社交和问题行为 神经发育障碍(NDDS)。我们的团队代表着 拥有人工智能(AI)、NDDS、诊断学、多模式等领域专长的计算机和临床科学家 干预措施和心理测量学。我们寻求以快速通道STTR赠款的形式提供支持，以验证 Argus-MDS的心理测量学特征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化治疗的开发 反映ASD患者社交、重复行为和相关症状特征的行为进展指标。 在第一阶段，将在黄金标准诊断评估期间收集视频和音频数据 房间隔缺损(ASD)15例。Aim 1.1将为关键社交网络建立Argus-MDS算法的质量和临床有效性 交流行为，而Aim 1.2将评估生物特征输出的重测可靠性。第一阶段将 显示Argus-MDS符合生物测定输出的质量指标，验证了临床医生- 技术人员反馈系统，并为自动化建立组内相关系数 社交通讯(AutoSC)输出。在第二阶段，我们的重点转向建立心理测量学 AutoSC分析的派生分数的特性，评估与已建立的临床和 功能措施，并为第三阶段的监管申报做准备。将从以下方面制定分数 通过社会沟通行为的探索性和验证性因素分析获得生物计量数据。目标 2.2评估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，制定全面的验证策略并执行分析性验证 控制法规和FDA的指导。第二阶段将从AutoSC输出中制定分数，评估 AutoSC得分的测量特征、Autos SC得分的信度和效度 根据战略文件和FDA指导执行所有分析验证。第一阶段和第二阶段 里程碑将为我们在第三阶段的商业化做好准备，包括申请监管批准和 产品发布。该项目的成功完成将提供一种新颖的、可扩展的医疗设备技术 支持对ASD和其他NDDS的社会损害进行客观、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.