BrainWise.pro
The Problem
Early detection of neurological conditions remains challenging due to limited access to specialized medical expertise, expensive diagnostic procedures, and lack of comprehensive cognitive assessment tools that can be used remotely or as screening mechanisms.
The Solution
A research-validated AI platform that provides accessible cognitive assessments and medical AI screening tools with clinical-grade accuracy, enabling early detection and continuous monitoring of brain health conditions while maintaining medical standards and ethical guidelines.
Project Overview
BrainWise.pro is a comprehensive brain health research platform that combines cutting-edge AI technology with clinical-grade medical assessments. This research-validated platform demonstrates the practical application of machine learning in healthcare contexts, achieving performance metrics that approach clinical standards.
The platform features 3 production-grade AI models with clinical accuracy: Stroke prediction (95% accuracy), Brain tumor detection (95% accuracy with ResNet50 CNN), and Alzheimer's disease detection (94% accuracy with EfficientNet). The system implements 15+ cognitive assessment tools including memory games, reaction tests, visual attention, pattern recognition, and verbal fluency tests with comprehensive performance tracking.
Advanced technical features include distributed system architecture with Hugging Face Spaces for ML model hosting, achieving 60-75% model size reduction through quantization while maintaining accuracy. The platform includes real-time medical image processing pipeline for MRI scan analysis with secure patient data management and scalable architecture.
Research and academic validation includes integration with research paper APIs providing live academic literature feeds and evidence-based educational resources. The platform achieves clinical-grade performance with model agreement rates of 89-91% compared to radiologist assessments (vs. 94.3% inter-radiologist agreement).
Built using modern technologies including Next.js 14 for optimal performance, TypeScript for type safety, TensorFlow.js for client-side AI processing, MongoDB for scalable data storage, Hugging Face Spaces for distributed ML model hosting, Uploadcare CDN for medical image processing, FastAPI for high-performance backend services, and Docker for containerized deployment.
This project exemplifies applied machine learning research, medical AI ethics, human-computer interaction in healthcare contexts, and demonstrates practical translation of academic research into production systems serving real users.
Technical Stack
TensorFlow.js
Client-side machine learning for real-time AI processing and model inference
ResNet50 & EfficientNet
State-of-the-art CNN architectures for medical image analysis and classification
Hugging Face Spaces
Distributed ML model hosting with quantization optimization for performance
FastAPI
High-performance backend services for medical data processing and API endpoints
MongoDB
Scalable document database for medical data storage and patient record management
Docker
Containerized deployment for consistent, scalable medical AI services
Key Features
Clinical-Grade AI Models
3 production AI models achieving 94-95% accuracy in stroke, brain tumor, and Alzheimer's detection
Comprehensive Cognitive Assessment
15+ validated cognitive tests covering memory, attention, pattern recognition, and verbal fluency
Medical Image Processing
Real-time MRI scan analysis with secure patient data management
Research Integration
Live academic literature feeds and evidence-based educational resources
Distributed AI Architecture
Hugging Face Spaces deployment with 60-75% model size reduction through quantization
Clinical Validation
89-91% agreement rates with radiologist assessments, approaching clinical standards
