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

基本信息

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
项目类别：
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

项目摘要

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.

阿格斯认知 STTR 拨款申请抽象的标准化的行为观察方法是发展、教育和行为不可或缺的一部分然而，现有的观察策略太费力，无法用于大规模干预。此外，目前的观察性研究还需要进行自闭症谱系障碍（ASD）的传播试验。战略无法产生足够的定量、可比和细粒度的评估，无法推动我们正在临床试验中比较疗法或干预的优化和个性化。开发微创医疗设备技术（“ARGUS-MDS”）以同时监测患有自闭症谱系障碍（ASD）及相关疾病的个体的多种关键社会行为和问题行为我们的团队代表了神经发育障碍 (NDD) 之间的重要合作。具有人工智能 (AI)、NDD、诊断、多模式专业知识的计算机和临床科学家我们寻求快速通道 STTR 拨款的支持来验证 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这将支持可扩展的数字治疗的开发。反映 ASD 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 ASD (n=15) 目标 1.1 将确定 ARGUS-MDS 算法对于关键社交的质量和临床有效性。通信行为，而目标 1.2 将评估第一阶段生物识别输出的重测可靠性。表明 ARGUS-MDS 满足生物识别输出的质量指标，验证了临床医生技术人员反馈系统，并建立自动化的组内相关系数在第二阶段，我们的重点转向建立心理测量。用于 AutoSC 分析的派生评分的特性，评估与已建立的临床和治疗的收敛性目标 2.1 的功能措施和监管备案准备工作。通过对社会沟通行为进行探索性和验证性因素分析来获取生物识别数据。 2.2 评估 AutoSC 分数与标准化临床评估分数的对应关系。根据医疗设备设计制定全面的验证策略并执行分析验证第二阶段将根据 AutoSC 输出进行评分，并进行评估。 AutoSC 分数的测量特征、Autos SC 分数的信度和效度，以及根据战略文件和 FDA 第一阶段和第二阶段指南执行所有分析验证。里程碑将为我们在第三阶段的商业化奠定基础，包括提交监管部门批准和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 NDD 中的社交障碍进行客观、自动化的临床评估。