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Tnzr/QSight-Care

Domain:

healthcare

Record type:

project
Creator:
Tnzr
Host:
Developed for Hack the Horizon by African Quantum Consortium. ## The Problem: Preventing Diabetic Blindness **Diabetic Retinopathy (DR)** is the leading cause of blindness among working-age adults worldwide. Early detection can prevent 95% of severe vision loss, but current challenges include: - **Limited screening access**: 50% of diabetics don't get regular eye exams - **Diagnostic delays**: Manual review by specialists takes days to weeks - **Resource inequality**: 80% of ophthalmologists serve only 20% of the population - **Progression risk**: DR progresses silently until vision loss occurs ## Our Vision: Quantum-Accelerated Screening **Q-Sight** aims to revolutionize diabetic retinopathy screening through quantum-enhanced artificial intelligence. Our vision is to create an accessible system that provides hospital-grade accuracy anywhere, anytime. ### **Core Innovation Goals** - **Quantum-optimized feature selection**: Intelligently reduce 512+ image features to critical biomarkers - **Hybrid quantum-classical architecture**: Leverage quantum advantages while maintaining classical reliability - **Accessible deployment**: Cloud-based solution requiring minimal local infrastructure ## Project Objectives ### **Primary Goals** 1. **Accuracy Target**: Achieve 92-95% classification accuracy on the APTOS 2019 dataset 2. **Quantum Advantage**: Demonstrate measurable improvement over classical-only approaches 3. **Clinical Utility**: Create an intuitive interface for healthcare professionals ### **Technical Milestones** - Phase 1: Classical baseline establishment (Days 1-3) - Phase 2: Quantum integration prototyping (Days 4-8) - Phase 3: System integration & validation (Days 9-12) - Phase 4: Presentation preparation (Days 13-14) ## Technology Strategy | Component | Technology Choice | Rationale | |-----------|------------------|-----------| | **Quantum Framework** | Qiskit (IBM Quantum) | Industry standard with excellent simulator support | | **Machine Learning** | PyTorch | Flexibility for custom quantum-classical in …