MAIA-QEDQuantum Enhanced Diagnostics

Integration Opportunities

POC Plan

This section explores how QED can complement state-of-the-art AI techniques for retinal anomaly detection, creating integrated workflows that leverage the strengths of both approaches with a focus on applications in eye care and neuroscience.

Complementary Technologies

QED and SOTA AI techniques address different aspects of the retinal imaging workflow:

  • AI Preprocessing: P-GAN can enhance low-quality OCT images before analysis, providing cleaner inputs for both diagnostic AI and QED's cognitive distillation.
  • Feature Extraction: AIU-DDNC can extract and classify retinal features, with results cognitively distilled by QED for efficient storage and transmission.
  • Cognitive Distillation: QED can reduce the outputs of AI analysis from 10GB to 2GB (80% reduction) while maintaining diagnostic quality (VMAF ≥95).
  • Biomarker Detection: QED's quantum-encoded features enable potential detection of Alzheimer's biomarkers in AMD patients, supporting neuroscience applications.

Integrated Workflow

An ideal integration would combine these technologies in a seamless workflow:

Input OCT Image/Video

10GB raw data from OCT device

P-GAN Enhancement

Improve contrast by 3.5x

AIU-DDNC Analysis

Feature extraction & classification

Diagnostic Results

Anomaly detection with 95% accuracy

QED Cognitive Distillation

80% size reduction (2GB output)

4K Reconstruction

ESRGANx4 enhancement with inferred details

Potential Hybrid Systems

Beyond simple pipeline integration, there are opportunities for deeper technological fusion:

  • Quantum-Cognitive Models: Leveraging QED's QAOA to optimize AI training and inference for retinal anomaly detection, achieving 35% faster feature extraction through MI300X's 4.5TB/s HBM3 and quantum pattern prioritization.
  • AI-Guided Cognitive Distillation: Using AI to identify diagnostically important regions in OCT images, allowing QED to prioritize these areas during cognitive encoding.
  • Federated Learning with Distilled Data: Enabling AI model training across institutions using QED-distilled data, reducing bandwidth requirements by 80%.
  • Therapeutic Response Tracking: Combining AI segmentation with QED's cognitive distillation to monitor DME fluid changes post-treatment with high precision (Dice score ≥0.90).

Implementation Considerations

Successful integration requires addressing several key considerations:

  • API Development: Creating standardized interfaces between AI systems and QED for seamless data exchange.
  • Quality Assurance: Implementing end-to-end testing to ensure diagnostic accuracy is maintained throughout the integrated workflow, targeting FDA 510(k) submission with ≥99.9% specificity.
  • Computational Resources: Balancing the resource requirements of AI processing and QED's cognitive distillation, with hybrid classical fallback (MI300X clusters) ensuring accessibility during the NISQ era.
  • Regulatory Compliance: Ensuring integrated systems meet medical device regulations and data protection requirements.

Case Study: Telemedicine Application

A practical example of integration would be a telemedicine platform for retinal screening:

  1. Data Acquisition: OCT images captured at remote clinics, potentially with lower-quality equipment.
  2. Enhancement: P-GAN enhances image quality, improving contrast and reducing noise.
  3. Analysis: AIU-DDNC analyzes the enhanced images, detecting and classifying anomalies.
  4. Cognitive Distillation: QED distills both the enhanced images and analysis results into 2GB structure.maia + key.maia files.
  5. Transmission: Distilled data sent to specialists for review, requiring 80% less bandwidth.
  6. Reconstruction: ESRGANx4 reconstructs 4K perspectives with inferred details for specialist review.

Clinical Applications

The integrated AI-QED solution offers significant benefits for specific clinical applications:

  • DME and RVO Monitoring: Remote monitoring of patients receiving treatments like Ozurdex, with therapeutic response fingerprints tracking fluid changes with high precision (Dice score ≥0.90).
  • AMD Management: Enhanced detection and classification of AMD features, with cognitive distillation enabling efficient longitudinal tracking.
  • Alzheimer's Screening: Potential non-invasive detection of Alzheimer's biomarkers in retinal images of AMD patients, with validation studies planned in partnership with Mayo Clinic.

Validation Strategy

A rigorous validation pathway is essential for clinical adoption:

  • AbbVie Partnership: Primary collaboration for DME/RVO therapeutic response validation and clinical trial integration, with direct access to Ozurdex patient data
  • Clinical Partnerships: Mayo Clinic for Alzheimer's biomarker validation, MSU for VMAF testing.
  • Dataset Bias Mitigation: Balanced training with diverse clinical datasets.
  • Regulatory Pathway: FDA 510(k) submission targeting ≥99.9% specificity.
  • IP Protection: Patent-pending quantum-cognitive methods creating defensible market space.