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：
9906771
负责人：
Attila Meretei
金额：
$ 13.97万
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 2020-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9906771
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 Health Healthcare Home environment 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 disability dissemination trial early childhood experimental study functional disability functional outcomes gaze improved 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.

Argus认知STTR赠款应用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科学研究。但是，现有的观察策略太实验室了，无法用于大规模干预和自闭症谱系障碍（ASD）所需的传播试验。另外，当前的观察策略不会产生足够的定量，可比和颗粒状评估，可以推动比较临床试验中的疗法或干预的优化和个性化。我们是开发最小的侵入性医疗设备技术（“ Argus-MD”）以简单地监视ASD及相关个人的多个关键社会和问题行为神经发育障碍（NDDS）。我们的团队代表了之间的重要合作具有人工智能专业知识（AI），NDD，诊断，多模式的计算机和临床科学家干预措施和精神计量学。我们以快速轨道授予的形式寻求支持，以验证 Argus-MD的心理测量特性及其在早期提供目标行为变化数据的能力童年和学龄儿童。然后，这将支持开发可扩展的数字处理反映了ASD中社会，重复行为和相关症状特征的行为的进度指标。在第一阶段，将在金标准诊断评估中收集视频和音频数据 ASD（n = 15）。 AIM 1.1将建立Argus-MDS算法的质量和临床有效性交流行为，而AIM 1.2将评估生物识别输出的重测可靠性。第一阶段会证明Argus-MD符合生物识别输出的质量指标，证实了临床 - 技术员反馈系统，并建立自动化的影响相关系数社交通信（AUTOSC）输出。在第二阶段，我们的重点转向建立心理测量学用于自动分析的派生得分的特性，评估与已建立的临床和功能措施，并准备在第三阶段进行调节申请。目标2.1。将从通过探索性和确认因素分析社会交流行为的生物识别数据。目的 2.2评估AUTOSC评分的对应关系对标准化临床评估的得分。目标2.3 根据医疗设备设计，制定了全面验证策略并执行分析验证控制法规和FDA指导。第二阶段将从Autosc输出中发展得分，评估 AUTOSC分数的测量特征，汽车SC分数的可靠性和有效性以及根据策略文档和FDA指导执行所有分析验证。第一阶段和第二阶段里程碑将使我们在第三阶段中进行商业化，包括申请法规批准和产品发布。该项目的成功完成将提供一种新颖的可扩展医疗设备技术为了支持目标，对ASD和其他NDD的社会障碍的自动临床评估。