Medical Diagnostics Platform
Neurological Testing Platform Web-based diagnostic tool using eye-tracking data
A platform that collects, processes, and visualizes eye-tracking data to assist doctors in neurological and ophthalmological assessments.
95%
Test Accuracy
Real-time
Analysis Time
Millions
Data Points
Instant
Processing Speed
Project Overview
Project Overview Neurological Testing Platform
This medical platform captures eye-tracking data through specialized hardware and processes it to support diagnosis.
Results are shown via interactive dashboards, charts, and reports to help doctors analyze neurological and vision-related conditions.
The system improves existing diagnostic methods by reducing manual interpretation and increasing measurement precision.
INDUSTRY
HealthTech / MedTech
PLATFORM
Web / Cloud
YEAR
2024
TYPE
B2С Platform
The Challenge
The Challenges
Diagnosis Limitations
Traditional methods were slow, subjective, and lacked precise digital data for effective analysis and decision-making.
01
Manual Data Interpretation
Relying on manual reading of test results slowed diagnosis and risked inconsistency.
02
Disjointed Tools
Multiple separate software and hardware tools complicated data collection and analysis workflows.
03
Poor User Experience
Interfaces were not optimized for clinical environments, reducing usability for healthcare professionals.
Product Experience
Product Experience
Diagnostic Workflow Support
The platform guides doctors through eye-tracking tests, showing real-time data and visualization to aid diagnosis.
Web Application
Interactive Diagnostic Dashboards
Live System
Time to Results
Minutes
Data Interpretation
Simplified
Diagnostic Accuracy
Improved
Workflow
Optimized
Real-time Data Collection
Eye-tracking data captured live via camera integration.
Data Calibration and Processing
Automated adjustment and analysis of raw eye-tracking data.
Visual Reports
Graphs and charts summarize patient results for easier interpretation.
Core Features
Core Features
Platform Capabilities
Key product capabilities include data capture, processing, visualization, and diagnostic reporting to support clinical decisions.
Eye-Tracking Data Collection
Supports integration with specialized eye-tracking hardware via WebSockets.
Data Calibration Engine
Applies algorithms to correct and standardize raw input data.
Interactive Visualization
Uses charts and graphs to present patient data clearly.
Diagnostic Dashboards
Centralized view for doctors to analyze and review test results.
Reporting Tools
Generates clinical reports from processed test data.
Our Approach
Our Approach
Development Focus Areas
Development was organized around key workstreams targeting performance, integration, and user interface design.
01
Real-Time Data Processing
Implemented continuous data handling from eye-tracking devices to ensure immediate and accurate input for analysis.
Deliverables
WebSocket-based data streaming
Real-time updates during tests
Stable data flow handling
Optimized performance
02
Data Analysis & Calibration
Developed logic for processing and calibrating raw eye-tracking data to ensure high diagnostic accuracy.
Deliverables
Calibration algorithms
Data transformation logic
Signal processing workflows
Structured diagnostic output
03
Visualization & UX
Designed intuitive dashboards and charts to present complex medical data in a clear and usable format.
Deliverables
D3.js charts and diagrams
Diagnostic dashboards
Data visualization workflows
User-friendly interface
Development Timeline
Development Timeline
Project Phases
The project progressed through research, development, integration, testing, and release over 18 months.
Phase 1
Research & Planning
Clinical requirements and testing workflows were defined.
Eye-tracking data collection approach was designed.
System architecture and initial UX concepts were planned.
Phase 2
Core Development
Frontend application and data processing logic were implemented.
Real-time data handling via sockets was established.
Initial UI for displaying diagnostic results was created.
Phase 3
Hardware Integration
Integration with eye-tracking camera systems was implemented.
Data acquisition and calibration mechanisms were developed.
Ensured accurate and synchronized data flow.
Phase 4
Visualization & Analytics
Interactive charts and diagrams were built using D3.js.
Diagnostic dashboards were created for clinical use.
Complex data was transformed into clear visual insights.
Phase 5
Testing & Validation
Unit, integration, and end-to-end testing were conducted.
System performance and stability were validated.
Platform was refined for real-world medical usage.
Technical Architecture
Technical Architecture
System Overview
Eye-Tracking Hardware
Specialized camera system capturing real-time eye movement data during neurological and ophthalmological tests.
Socket Communication
Real-time data transfer from the camera to the application for further processing and analysis.
Calibration Engine
Processes and calibrates raw eye-tracking data to improve measurement accuracy.
Data Analysis Logic
Transforms captured signals into structured diagnostic data for medical evaluation.
Real-Time Processing
Supports continuous updates during testing sessions.
Diagnostic Dashboards
Displays processed test results in a clear interface for doctors and healthcare professionals.
Charts & Diagrams
D3.js-based visualizations for neurological and ophthalmological test results.
Reporting Views
Helps review, compare, and interpret patient test outcomes.
Frontend Application
Angular-based web interface for managing tests, viewing results, and navigating diagnostic workflows.
API & Service Integration
Connects the frontend with backend services and diagnostic data sources.
Testing & Quality Layer
Supports reliability through unit, integration, and end-to-end testing practices.
Real-time Data Processing
Processes incoming eye-tracking data with minimal latency.
Data Visualization
Interactive charts built with D3.js present patient results clearly.
Calibration Module
Standardizes raw sensor data for consistent diagnostic use.
Team & Collaboration
Team Collaboration
Project Roles
A cross-functional team of 50 professionals followed Agile Scrum with sprint-based workflows.
Frontend Developers
Built UI components, managed real-time data visualization.
Backend Developers
Handled data calibration logic and hardware communication.
QA Engineers
Performed functional tests and ensured system stability.
Designers
Developed UI/UX suited for medical professionals.
Product Managers
Coordinated requirements and release planning.
Agile Process
Regular sprint planning and reviews supported iterative improvement.
Cross-Functional Teams
Close collaboration reduced handoff delays and supported fast issue resolution.
Project Facts
Product Type
Medical Diagnostic Platform
Industry
HealthTech / MedTech
Platform
Web / Cloud
Users
Doctors & Healthcare Professionals
Technology Stack
Technology Stack
Tools and Frameworks
The platform leverages Angular, TypeScript, RxJS, D3.js, and WebSockets for a responsive web experience.
Frontend
Angular 9
Framework
TypeScript
Language
SCSS
Styling
Real-Time Data
RxJS
Reactive Data Handling
WebSockets
Real-time Data Streaming
Visualization & Analytics
D3.js
Data Visualization
Custom Charts
Medical Data Representation
Impact & Results
Impact and Results
Project Outcomes
The platform supports faster, more accurate diagnostics with improved usability and automation for healthcare teams.
Faster Diagnostic Workflows
Reduced time required to collect and interpret eye-tracking data.
Improved Accuracy
Data-driven analysis reduces subjective interpretation.
Clear Data Visualization
Charts and dashboards make complex data easier to understand.
Increased Automation
Less manual processing lowers error risks and effort.
Key Outcomes
Faster workflows
More accurate diagnosis
Better patient data insight
Higher clinician satisfaction
SUP.AI transformed how we operate. What used to take our team hours now happens automatically in seconds. The platform does not just automate tasks — it makes intelligent decisions that improve our business every day.
Michael Chen
Chief Operations Officer
Enterprise Technology Company
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