Intelligent Eye Disease Detection and Treatment Support
Analyze patient age, symptoms, laboratory findings, OCT scans, fundus images, radiological results, and family history to identify potential eye diseases and receive evidence-based treatment recommendations.
~90%
Detection Accuracy
24/7
Instant Analysis
Secure
Patient Records
Multimodal Diagnostics
v1: textual inference available currentlyCombine text clinical data with ocular imaging for comprehensive risk stratifications.
AI Clinical Support
Automated image segmentation and feature identification to assist clinical decision making.
Patient Records
Structured archiving of clinical findings, serial scans, and longitudinal patient reports.
Compliant with global healthcare & data regulatory standards
Engineered for Precision Ophthalmology
Empower clinicians with automated AI-assisted diagnostics, rapid report generation, and secure record keeping.
Multimodal Deep Learning
Evaluates OCT layer segmentations alongside clinical text history for holistic diagnosis.
Structured Reporting
Generates standardized medical reports exportable into institutional EHR platforms.
Longitudinal Tracking
Monitors disease progression over serial patient visits with comparative analytics.
Encrypted Records
End-to-end encryption for patient protected health information (PHI).
Real-Time Inference
Sub-second inference times for rapid clinical decision-making during examinations.
Diagnostic Assist
Highlights regions of interest on fundus scans automatically for clinician validation.
Advanced AI Diagnostic Capabilities
Built to streamline clinical workflows without replacing clinician decision authority.
AI Diagnostic Analysis
Fast multi-label classifications.
Rapid Imaging Screening
Batch upload fundus images for rapid queue processing.
Ocular Feature Extraction
Automatic cup-to-disc ratio and lesion localization.
Automated Clinical Reports
Export standardized diagnostic summaries for patient charts.
Exportable Analytics
Download PDF and structured JSON reports seamlessly.
Centralized Repository
Unified patient database with queryable scan histories.
How Diagnostic Analysis Works
Text Analysis Process
Input patient demographics, chief complaints, and clinical history
NLP algorithms parse and structure unstructured medical notes
Identify key clinical concepts, symptoms, and potential risk factors
Generate preliminary diagnostic considerations and suggested next steps
Frequently Asked Questions
Contact Us
Have questions or need technical support? Get in touch with our team.