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.

项目总结