Conjunctivitis is one of the most common complaints in primary care clinics and emergency departments worldwide. With millions of cases diagnosed annually, it represents a significant burden on healthcare systems — often leading to unnecessary emergency room visits when patients and community healthcare providers lack the tools to confidently assess the condition. The central challenge facing clinicians is differentiating between the three main types: bacterial, viral, and allergic. This distinction is critical because only bacterial conjunctivitis requires antibiotic treatment, yet approximately 60% of patients currently receive unnecessary antibiotic prescriptions.
ProdigEye was developed as a Software as a Medical Device (SaMD) to address these challenges, offering general practitioners, pediatricians, and other community healthcare professionals an accurate, accessible decision-support tool. By delivering rapid, reliable diagnostic guidance at the point of care, ProdigEye helps reduce unnecessary ER visits, shortens the time to appropriate treatment, and empowers frontline clinicians with the information they need to manage conjunctivitis cases confidently in community settings.
Smartphone-Based Platform
One of the core advantages of the ProdigEye system is its exceptional accessibility. The platform operates on standard smartphones and does not require the purchase or operation of specialized medical equipment. Healthcare providers can capture an image of the patient's eye using a regular phone camera, and the system performs the analysis automatically. This approach enables broad deployment across community clinics and rural hospitals, where images may be captured under clinician guidance or as part of telemedicine workflows. The application features an intuitive user interface encompassing patient registration, image capture, analysis, and results presentation — the entire process takes less than three minutes.
Clinical Design Principles: Our platform was designed from the ground up with input from practicing ophthalmologists and primary care physicians. This clinical collaboration ensures that ProdigEye seamlessly integrates into existing workflows while addressing the real-world challenges clinicians face daily in diagnosing eye infections.
Advanced Deep Learning Image Analysis
At the heart of ProdigEye lies a proprietary deep neural network architecture specifically optimized for ophthalmic image analysis. Unlike generic image recognition systems, our AI engine was developed exclusively for detecting clinical signs of bacterial conjunctivitis, trained on a carefully curated dataset of clinically validated eye images.
The image analysis pipeline incorporates multiple processing stages, progressively identifying visual signs that indicate bacterial infection — from basic color and texture patterns to complex clinical indicators that correlate with the diagnosis.
Proprietary Architecture: Our neural network architecture incorporates novel optimizations developed by our research team, enabling high accuracy while maintaining fast inference times suitable for mobile deployment. The specific design elements and training methodology represent core intellectual property developed through years of research and clinical validation.
Medical-Grade Accuracy
Trained on clinically validated ophthalmic images with confirmed diagnoses from board-certified ophthalmologists.
Real-Time Processing
Optimized inference engine delivers results in under 3 seconds, enabling seamless integration into clinical workflows.
Device Agnostic
Advanced image normalization ensures consistent performance across different smartphone cameras and lighting conditions.
Privacy First
On-device processing options available to meet stringent healthcare data protection requirements.
Clinical Questionnaire for Enhanced Diagnosis
Beyond image analysis, ProdigEye incorporates an additional layer of clinical intelligence through a targeted questionnaire. This questionnaire is designed to refine the differential diagnosis between different types of conjunctivitis, as each type has unique clinical characteristics that may not always be visible in an image alone.
Evidence-Based Question Design: Our clinical questionnaire was developed in collaboration with leading infectious disease specialists and refined using retrospective clinical data and real-world case analysis. The specific questions and their weighted integration with image analysis represent a unique clinical decision support methodology protected by our intellectual property portfolio.
Multi-Stage Analysis Pipeline
This multi-stage pipeline combines computer vision analysis with clinical questionnaire data through our proprietary fusion algorithm, offering a comprehensive assessment that is more accurate than either component alone. The integration reflects the established medical approach where physicians combine physical examination findings with detailed patient history.
Results and Treatment Recommendations
Upon completion of the analysis, ProdigEye generates a comprehensive report. The system calculates a risk score that classifies patients into low-risk or high-risk categories for bacterial conjunctivitis. The classification threshold was carefully calibrated to optimize sensitivity, ensuring that true bacterial infections are rarely missed.
Performance metrics were derived from an internal validation study conducted on a dataset of 76 eye images, evenly balanced between bacterial conjunctivitis cases and non-bacterial controls. Results are preliminary and may vary depending on patient population, image quality, and clinical setting.
Beyond classification, the system suggests tailored treatment recommendations based on the identified risk level. For high-risk cases, topical antibiotic therapy may be considered along with appropriate follow-up. In all cases, the system emphasizes that it serves as a clinical decision support tool and does not replace professional medical judgment.
The final report can be saved as medical documentation (EMR) for ongoing patient care, ensuring all clinical findings and AI analysis results are in accordance with applicable healthcare data protection regulations.
Summary: ProdigEye integrates advanced deep learning with evidence-based clinical practice to deliver exceptional diagnostic accuracy via standard smartphones. As a SaMD, it supports GPs, pediatricians, and community healthcare professionals by reducing ED referrals, shortening time to appropriate care, and combating antibiotic overuse — improving both patient outcomes and healthcare efficiency.