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.

项目概要 新生儿科医生经常需要识别可能患有神经发育不良的婴儿 结果，包括脑瘫（CP）。 CP 是美国儿童中最常见的运动障碍 并且与低胎龄体重、早产和中风等危险因素有关。虽然核磁共振成像 和颅脑超声在早产期提供有价值的结构信息，它们具有中等预测性 早期 CP 风险识别的准确性。过去20年，大量研究证实了其临床潜力 全身运动评估（GMA）用于早期脑瘫风险识别，文献中达成共识： GMA 提供最高的准确度。第一阶段“狭窄同步”一般运动 (CSGM) 跨越 34-48 “扭动运动”阶段和第二阶段“强迫、随意运动”期间的孕周 (GA) 跨越 50-59 周的 GA 已表现出对发展 CP 的高敏感性和特异性，同时范围 由训练有素的专家执行时，效率可达 80%-98%。 尽管 GMA 具有潜力，但只有极少数临床中心可以使用，因为采用和常规应用取决于 是否有训练有素的 GMA 评估员来进行冗长且昂贵的床边观察或视频审查 - 基于评分和手动报告创建。提出了脑瘫风险识别系统（CPRIS），该系统将 成为第一个将 GMA 自动化用于日常应用的公司。 CPRIS 构成了下一代方法，它将 通过用客观、系统、经过验证的运动模式取代评估者的视觉格式塔，从根本上改变 GMA 分类。此外，CPRIS 可能提供一种告知和评估新兴干细胞功效的方法。 沿着早期发育连续体对 CP 进行基于细胞的干预。 第一阶段和第二阶段的成功实施将完成小型化、移动化、高度自动化 用于脑瘫风险识别的预生产系统，工作人员、临床医生和健康人员可以轻松应用 护理人员无需进行任何形式的手动后处理操作或视频文件传输。一个集成的 该实用程序将支持 GMA 创建以及与电子健康记录 (EHR) 系统的报告共享。一个应用程序—— 特定的、完全集成的设备将实现最高程度的标准化，从而实现最高的数据质量。 在两个著名的 3 级新生儿重症监护病房 (NICU) 进行的现场研究中，将使用“RGB-D”或 3D 来获取婴儿运动 “深度”相机与特定于应用和阶段的“深度流”卷积神经网络相结合 （CNN）分类器方法，不需要婴儿接触（与运动学方法相比）并捕获整体 身体动作。这项工作标志着首次利用此类技术来实现 GMA 自动化。将比较结果 高级 GMA 评估者对第一阶段和第二阶段高风险早产儿样本的一致决定。 新方法的可行性将通过 ROC-AUC 分析确定，成功阈值≥ 0.90 准确性。总体结果将由各领域公认专家组成的咨询委员会进行评估 新生儿学、儿科、脑瘫、GMA 和生物统计学。