Clinical Decision Support

Early Diagnosis with AI-Assisted Eye Movement Analysis (VOG)

RAVENEYE analyzes VOG-based eye-movement data with AI, adding an objective and visual decision-support layer to clinical evaluation.

Explore system flow
VOG Analysis
RAVENEYE VOG analysis interface
Eye-movement signalSaccade, microsaccade, memory/anti-saccade, smooth pursuit, fixation
HeatmapClear reporting with heatmaps and graphs
Clinical reportDisease stage estimation & clinical report
VOG Scope

VOG-based eye-movement measurements, AI-driven objective analysis, early diagnosis

AI Method

Deep learning for automated feature extraction, data augmentation, and modeling

DATA Data Management

Turkey’s first large-scale eye-movement database built with anonymized patient data

TRL TRL

Development stage focused on clinical validation

Clinical Value

Eye-movement data becomes readable diagnostic-support output.

The system combines data collection, anonymization, AI analysis and reporting into one clinical flow. It does not replace physician judgment; it makes measurable findings easier to see.

Rapid setup and clinical applicability with portable VOG
Automated and objective assessment (physician decision support)
Continuously learning models via an anonymized central data pool
Clear reporting with heatmaps and graphs
System Flow

A four-step analysis line from VOG measurement to clinical report

01 Data collection with VOG (clinical task sets)
02 Anonymization and transfer to the central data pool
03 Automated analysis with deep learning models
04 Disease stage estimation & clinical report
Analysis Layers

Objective measurement, central data and readable visualization

Objective

  • Compare eye movements in neurodegenerative diseases with healthy individuals
  • Evaluate eye movements of different groups objectively
  • Support clinical diagnosis at early stages

Method

  • VOG-based, precise and comprehensive device infrastructure
  • Analysis of eye-tracking data with 3D visual tasks
  • Deep learning for automated feature extraction, data augmentation, and modeling
  • Visualization of results with graphs/heatmaps

Expected Outcome

  • A new screening test contributing to early diagnosis
  • Improved diagnostic workflows and cross-specialty awareness
  • Stronger clinical management and better patient quality of life
Project Frame

Project Info

Program TÜBİTAK 1501 - Industry R&D Projects
Scope VOG-based eye-movement measurements, AI-driven objective analysis, early diagnosis
Clinical Inputs Saccade, microsaccade, memory/anti-saccade, smooth pursuit, fixation
Solution Components Portable VOG · Central Data Pool · Automated Analysis · Visualization
Target Use Neurology & Ophthalmology clinics, screening/follow-up
Stakeholders Hacettepe Univ. Ophthalmology/Neurology Depts & Raventech
TRL Development stage focused on clinical validation
Data Management Turkey’s first large-scale eye-movement database built with anonymized patient data