AI-Powered Conjunctivitis
Detection System
Revolutionizing eye health diagnosis with 97% accuracy and instant detection capabilities
Executive Summary
Medical Challenge
Healthcare professionals face difficulties in quickly and accurately diagnosing conjunctivitis (pink eye), leading to delayed treatment and potential complications. Manual diagnosis requires significant clinical expertise and time, creating bottlenecks in patient care.
AI Solution
Advanced deep learning system utilizing U-Net++ architecture for precise eye image segmentation and classification. The model processes eye images in real-time, delivering clinical-grade diagnostic accuracy while reducing healthcare provider workload.
Clinical Impact
97% diagnostic accuracy with instant results, dramatically reducing consultation time while improving patient outcomes. The system enhances healthcare accessibility and provides consistent diagnostic quality across different healthcare settings.
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Detection Results
Consult ophthalmologist for treatment
Key Findings
Detection Accuracy
Conjunctivitis diagnosis precision
Analysis Speed
Average processing time
Model Efficiency
U-Net++ performance improvement
Clinical Performance Analysis
Diagnostic Accuracy
Clinical validation results
Response Time
Real-time diagnosis
Training Images
Comprehensive dataset
Architecture
Advanced segmentation
Technical Solutions
Image Processing Pipeline
Image Preprocessing
Segmentation
Classification
Technology Stack
Python
Core Development Language
Data processing & ML implementation
TensorFlow
Deep Learning Framework
U-Net++ model training & inference
Flask
Web Application Framework
API development & web interface
OpenCV
Computer Vision Library
Image preprocessing & manipulation
Clinical Impact
Executive Summary
Healthcare Challenge
Manual conjunctivitis diagnosis requires significant clinical expertise and time, creating bottlenecks in patient care
AI Solution
Automated deep learning system providing instant, accurate conjunctivitis detection with clinical-grade reliability
Clinical Results
- • 97% diagnostic accuracy achieved
- • 30-second analysis time
- • Enhanced patient care quality
- • Reduced healthcare provider workload
Clinical Detection Interface
Diagnostic Accuracy
Clinical validation
Analysis Speed
Instant diagnosis
Efficiency Gain
Healthcare optimization
Improved Patient Care
Faster, more accurate diagnosis enabling immediate treatment decisions and improved patient outcomes.
- 97% diagnostic accuracy reduces misdiagnosis
- Instant results enable immediate treatment
- Consistent quality across healthcare settings
Healthcare Accessibility
Democratizing expert-level eye care diagnosis, especially beneficial for underserved areas and remote locations.
- Enables remote consultations and telemedicine
- Reduces need for specialist referrals
- Cost-effective healthcare delivery
Expanded Diagnostic Potential
The system demonstrates significant potential for detecting other red-eye conditions beyond conjunctivitis, making it a versatile tool for comprehensive eye health care.
Allergic Conjunctivitis
Detection of allergy-related eye inflammation patterns
Dry Eye Syndrome
Identification of tear film abnormalities and dryness
Corneal Infections
Early detection of corneal inflammation and damage
Clinical Implementation Success
Transforming eye care through intelligent automated diagnosis