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Real-Time AI-Driven CNS Monitoring System Development

darly solutions

Medical

Health & Fitness

Challenges in Real-Time CNS Data Processing and Visualization

Chiropractors rely on outdated X-ray and manual assessment methods that lack real-time insights into CNS activity. Existing solutions fail to provide immediate brainwave and HRV data visualization, delaying treatment evaluation. Complex hardware-software integration, cross-team collaboration inefficiencies, and scalability limitations hinder timely market delivery of advanced neuroscanning systems.

About the Client

A US-based healthtech startup specializing in AI-powered neuroscanning solutions for chiropractic care

Key Objectives for CNS Monitoring System Development

Accelerate time-to-market by 10% through optimized development workflows
Enable seamless collaboration between three distributed development teams
Create scalable architecture supporting future feature integration
Develop intuitive tablet-based data visualization for real-time CNS monitoring
Implement secure, low-latency data processing pipeline for 20+ hardware parameters

Core System Functionalities and Features

BLE protocol integration for hardware parameter streaming
3D real-time CNS activity visualization using Three.js
Patient history tracking with comparative analysis tools
Cross-platform tablet application with React Native framework
AI-powered anomaly detection in brainwave patterns

Technology Stack Requirements

React Native

Three.js

C++

SQLite

TypeScript

System Integration Needs

BLE hardware communication
AI analytics engine
Cloud storage API

Performance and Scalability Criteria

Sub-50ms latency for real-time visualization
Horizontal scalability for 10x user growth
HIPAA-compliant data security framework
99.9% system uptime guarantee
Modular architecture for feature updates

Expected Impact on Clinical Efficiency and Data Accuracy

The solution will reduce data processing time by 23% through optimized C++ algorithms and BLE stream management, enabling chiropractors to make immediate treatment adjustments based on validated CNS metrics. Scalable architecture will support nationwide deployment while maintaining sub-second visualization updates, improving diagnostic confidence and patient outcomes through quantifiable progress tracking.