Cerebral Palsy Risk Identification System

脑瘫风险识别系统

• 批准号: 10709554
• 负责人: JAMES P O'HALLORAN
• 金额: $ 25.37万
• 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/10709554
• 关键词: 3-Dimensional; Adoption; Advisory Committees; Benchmarking; Biometry; Birth; Birth Weight; Cephalic; Cerebral Palsy; Certification; Child; Childhood; Classification; Clinical; Complex; Consensus; Data; Data Set; Databases; Development; Device or Instrument Development; Devices; Distal; 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; Production; Progress Reports; Reporting; Risk; Risk Assessment; Risk Factors; Sample Size; Sampling; Scoring Method; Sensitivity and Specificity; Site; Software Validation; Standardization; Stroke; System; Technology; Testing; Time; Training; United States; Video Recording; Visual; Weight; clinical application; clinical center; computerized; computerized data processing; convolutional neural network; cost; data quality; design; disability; efficacy evaluation; electronic health record system; 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 新生儿学家经常被要求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 并且与危险因素相关，包括低于胎龄体重、早产和中风。虽然MRI 和颅超声提供有价值的结构信息在早产期，他们有中度预测 早期CP风险识别的准确性。在过去的20年里，许多研究已经证实了临床潜力 一般运动评估（GMA）用于早期CP风险识别，文献中达成共识， GMA提供最高的精度。第一阶段“狭窄的同步”一般动作（CSGMs），跨度34-48 在“扭动运动”期间和第2阶段“强迫、自愿运动”期间， 在50-59周的时间内，GA对CP的发展表现出高灵敏度和特异性， 由经过广泛培训的专家执行时，从80%-98%。 尽管具有潜力，但GMA在极少数临床中心可用，因为采用和常规应用取决于 是否有训练有素的GMA评分员进行冗长而昂贵的床边观察或视频审查- 基于评分和手动报告创建。脑性瘫痪风险识别系统（CPRIS）的建议，将 成为第一个将GMA自动化用于日常应用的公司。CPRIS是下一代方法， 从根本上改变GMA，用客观、系统、有效的运动模式取代评分员的视觉完形 分类.此外，CPRIS可能提供一种告知和评估新兴干细胞治疗有效性的方法。 沿着早期发育连续体进行基于细胞的干预。 第一和第二阶段的成功实施将完成一个小外形、移动的、高度自动化的 脑瘫风险识别的预生产系统，可以很容易地应用于工作人员，临床医生和健康 护理人员无需任何形式的手动后处理操作或视频文件传输。集成 该实用程序将支持GMA创建和与电子健康记录（EHR）系统共享报告。任何申请─ 特定的、完全集成的设备将实现最高程度的标准化，从而提高数据质量。 在两个著名的3级NICU的现场研究中，将使用“RGB-D”或3D “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)分类方法，不需要婴儿接触（与运动学方法相比），并捕捉整个- 肢体动作这一努力标志着首次利用这种技术使全球海洋环境状况评估自动化。将比较结果 在第1阶段和第2阶段的高风险早产儿样本中，对高级GMA评定者的一致确定。 新方法的可行性将通过ROC-AUC分析确定，成功阈值≥ 0.90 精度由下列领域公认专家组成的咨询委员会将对总体成果进行评估： 小儿科、脑性瘫痪、GMA和生物统计学。