# Cerebral Palsy Risk Identification System

> **NIH NIH R43** · NEUROCOMP SYSTEMS, INC. · 2023 · $253,697

## Abstract

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.
Viabil...

## Key facts

- **NIH application ID:** 10709554
- **Project number:** 5R43NS132529-02
- **Recipient organization:** NEUROCOMP SYSTEMS, INC.
- **Principal Investigator:** JAMES P O'HALLORAN
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $253,697
- **Award type:** 5
- **Project period:** 2022-09-20 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10709554

## Citation

> US National Institutes of Health, RePORTER application 10709554, Cerebral Palsy Risk Identification System (5R43NS132529-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10709554. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*