Comparison

Retinal anomaly detection using Optical Coherence Tomography (OCT) is critical for diagnosing and managing diseases like Age-related Macular Degeneration (AMD), Diabetic Macular Edema (DME), and Retinal Vein Occlusion (RVO). Beyond eye care, OCT imaging offers insights into neurodegenerative diseases like Alzheimer's. However, challenges such as large data sizes (e.g., 10GB per patient dataset), variable image quality, and limited telemedicine scalability hinder progress. This section compares State-of-the-Art (SOTA) AI techniques with QED, a cognitive-quantum solution that achieves 80% data reduction (10GB to 2GB) while maintaining diagnostic accuracy (95% agreement).

SOTA AI Techniques Overview

SOTA AI techniques have significantly advanced retinal anomaly detection. The NIH's P-GAN (2024) enhances OCT imaging 100x faster than manual methods, with a 3.5x contrast improvement, making it ideal for evaluating AMD and DME. Deep learning models, such as those using VGG16, achieve 93-99% accuracy in classifying retinal diseases like AMD, DME, and Drusen.

However, these techniques face limitations: P-GAN requires substantial GPU resources (e.g., 16GB VRAM for real-time processing), and deep learning models lack cognitive understanding, resulting in large datasets (e.g., 10GB per patient) that hinder telemedicine applications. Additionally, cross-device variability (e.g., Optovue vs. Heidelberg OCT systems) can reduce generalization, with accuracy dropping by 5-10% across devices. These gaps highlight the need for a solution that combines diagnostic accuracy with efficient data management.

QED's Cognitive-Quantum Approach

QED's qodec (quantum-cognitive encoder) introduces a paradigm shift in retinal anomaly detection, moving beyond traditional compression to a cognitive framework that understands and reconstructs OCT data. Unlike SOTA AI techniques that focus on pixel-level analysis, QED's Perceptual Decomposition Engine extracts semantic (YOLOv8+CLIP), motion (sparse RAFT), and fractal-edge layers, discarding diagnostically irrelevant data while maintaining VMAF ≥95.

This cognitive distillation reduces OCT datasets from 10GB to 2GB (structure.maia + key.maia files), an 80% reduction that enables efficient telemedicine. When reconstructed through ESRGANx4, the system can enhance details beyond the original resolution, supporting 4K perspectives with 95% diagnostic agreement for AMD, DME, RVO, CNV, and Drusen, validated across OCTDL, Kermany, OCTID, and RETOUCH datasets.

QED's Quantum Approximate Optimization Algorithm (QAOA) balances VMAF and data reduction, achieving 35% faster feature extraction through MI300X's 4.5TB/s HBM3 and quantum pattern prioritization. This cognitive-quantum approach enables telemedicine by transmitting only diagnostically relevant data, supports clinical trials with efficient data management, and provides potential insights into Alzheimer's biomarkers in AMD patients.