Cerebral Palsy Risk Identification System

脑瘫风险识别系统

• 批准号: 10545159
• 负责人: JAMES P O'HALLORAN
• 金额: $ 24.31万
• 依托单位: NEUROCOMP SYSTEMS, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-20 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10545159
• 关键词: 3-Dimensional; Adoption; Advisory Committees; Benchmarking; Biometry; Birth; Cephalic; Cerebral Palsy; Certification; Child; Childhood; Classification; Clinical; Clinical assessments; Complex; Computer software; Consensus; Data; Data Set; Databases; Development; Device or Instrument Development; Devices; Distal; Electronic Health Record; Elements; Ensure; Environment; Gestational Age; Goals; Health Personnel; Human Resources; Infant; Intervention; Literature; Magnetic Resonance Imaging; Manuals; Methods; Motor; Movement; Multicenter Studies; Muscle Cramp; Neonatology; Outcome; Outpatients; Pattern; Pediatrics; Performance; Phase; Phenotype; Positioning Attribute; Premature Birth; Premature Infant; Process; Progress Reports; Reporting; Risk; Risk Assessment; Risk Factors; Sample Size; Sampling; Scoring Method; Sensitivity and Specificity; Site; Standardization; Stroke; System; Technology; Testing; Time; Training; United States; Video Recording; Visual; Weight; base; clinical application; clinical center; computerized; computerized data processing; convolutional neural network; cost; data quality; design; disability; experience; field study; high risk; inclusion criteria; kinematics; machine learning classifier; meetings; neural network classifier; next generation; novel strategies; operation; physically handicapped; software systems; stem cells; success; ultrasound; wireless

PROJECT SUMMARY Neonatologists are often required to identify infants who are likely to suffer poor neurodevelopmental outcomes, including Cerebral Palsy (CP). CP is the most common motor disability among children in the United States and is associated with risk factors including low weight for gestational age, premature birth, and stroke. Although MRI and cranial ultrasound provide valuable structural information in the preterm period, they have moderate predictive accuracy for early CP risk identification. Over the past 20 years, numerous studies have validated the clinical potential of General Movement Assessment (GMA) for early CP risk identification and there is consensus in the literature that GMA offers the highest accuracy. Stage 1 “cramped synchronized” general movements (CSGMs) spanning 34-48 weeks gestational age (GA) during the “writhing movements” period and Stage 2 “forced, voluntary movements” spanning 50-59 weeks GA have demonstrated high sensitivity and specificity for developing CP, conjointly ranging from 80%-98% when performed by extensively trained experts. Despite its potential, GMA is available in very few clinical centers, as adoption and routine application depend on the availability of highly trained GMA raters to perform lengthy and costly bedside observations or video review- based scoring and manual report creation. A Cerebral Palsy Risk Identification System (CPRIS) is proposed that will be the first to automate GMA for routine application. The CPRIS constitutes a next-generation approach that will fundamentally transform GMA by replacing rater visual gestalts with objective, systematic, validated movement pattern classification. Further, the CPRIS potentially offers a means of informing, and assessing the efficacy of emerging stem cell-based interventions for CP along the early developmental continuum. Successful implementation of Phase I&II will complete a small form factor, mobile, highly automated preproduction system for cerebral palsy risk identification that can be readily applied by staff, clinicians, and health care provider personnel without any form of manual post-processing operations or video file transfer. An integrated utility will support GMA creation and report sharing with Electronic Health Record (EHR) systems. An application- specific, fully integrated device will achieve the highest degree of standardization and thus data quality. In a field study at two prominent Level 3 NICUs, infant movements will be acquired using an “RGB-D”, or 3D “depth” camera in conjunction with an application- and stage-specific “Depth-Flow” convolutional neural network (CNN) classifier approach, that requires no infant contact (contrasting with kinematic methods) and captures whole- body movements. This effort marks the first utilization of such technology to automate GMA. Results will be compared to consensus determinations of advanced GMA raters in a sample of high risk preterm infants at both Stages 1 & 2. Viability of the new approach will be determined by ROC-AUC analyses, with a threshold for success of ≥ 0.90 accuracy. Overall results will be evaluated by an Advisory Committee of recognized experts in the fields of neonatology, pediatrics, cerebral palsy, GMA and biostatistics.

项目摘要 新生儿学家通常需要识别可能遭受不良神经发育的婴儿 结果，包括脑瘫（CP）。 CP是美国儿童中最常见的运动残疾 并与危险因素有关，包括胎龄低体重，早产和中风。虽然MRI 颅骨超声在早产期提供有价值的结构信息，它们具有适度的预测性 早期CP风险识别的准确性。在过去的20年中，许多研究验证了临床潜力 一般运动评估（GMA）用于早期CP风险识别，文献中达成共识 GMA提供了最高的精度。阶段1跨越34-48的“狭窄同步”通用运动（CSGM） 在“写作运动”期间和第2阶段“强迫，自愿运动”期间妊娠年龄（GA） 跨越50-59周的GA表现出高灵敏度和发展CP的特异性 由经过广泛培训的专家执行时，从80％-98％开始。 尽管具有潜力，但GMA在很少的临床中心中可用，因为采用和常规申请取决于 关于训练有素训练的GMA评估者的可用性，以执行冗长且昂贵的床边观察或视频评论 - 基于评分和手动报告的创建。提出了一个脑瘫风险识别系统（CPRIS） 成为第一个自动化GMA进行常规应用程序的人。 CPRI构成了下一代方法 从根本上通过客观，系统，有效的运动模式替换评估者视觉格式塔来改变GMA 分类。此外，CPRIS有可能提供一种通知和评估新兴STEM的效率的方法 基于细胞的CP沿着早期发育连续体的干预措施。 第一阶段和第二阶段的成功实施将完成一个小型，移动性，高度自动化 脑性麻痹风险识别的预生产系统可以由工作人员，临床医生和健康很容易应用 护理提供者的人员没有任何形式的手动后处理操作或视频文件传输。一个集成的 实用程序将支持GMA创建并与电子健康记录（EHR）系统报告。申请 - 特定的，完全集成的设备将达到最高程度的标准化，从而达到数据质量。 在两个著名3级NICUS的现场研究中，将使用“ RGB-D”或3D获取婴儿运动 “深度”摄像机与应用和阶段特定的“深度”卷积神经网络结合 （CNN）分类器方法，不需要婴儿接触（与运动学方法形成鲜明对比），并捕获了整个 身体运动。这项工作标志着这种技术首先利用了自动化GMA。结果将被比较 在两个阶段和第2阶段的高风险早产剂样本中对高级GMA评估者的共识确定。 新方法的生存能力将通过ROC-AUC分析确定，成功的阈值≥0.90 准确性。总体结果将由公认专家的咨询委员会评估 新生儿学，儿科，脑瘫，GMA和生物统计学。