MAIA-QEDQuantum Enhanced Diagnostics

State-of-the-Art (SOTA)

POC Plan

Recent advancements in AI-based retinal anomaly detection have significantly improved diagnostic capabilities, speed, and accuracy. This section summarizes key developments post-2024.

P-GAN (NIH, 2024)

The NIH's P-GAN, released in April 2024, represents a breakthrough in OCT imaging enhancement:

  • Enhances OCT imaging 100x faster than previous methods
  • Improves contrast by 3.5x, making anomalies more visible
  • Reduces noise and artifacts that commonly obscure retinal details
  • Open-source implementation available for research and clinical integration

Deep Learning Models

Advanced deep learning architectures have achieved remarkable accuracy in classifying retinal diseases:

  • Classification accuracy ranging from 93-99% for major retinal conditions
  • Reduced false positives by 42% compared to 2023 models
  • Improved generalization across diverse patient populations and imaging equipment

Telemedicine Integration

AI systems for remote screening have transformed care delivery:

  • Reduced unnecessary specialist referrals by 38%
  • Enabled real-time preliminary diagnosis in remote settings
  • Standardized interpretation across different clinical environments

SOTA Timeline

P-GAN Release

NIH releases P-GAN, enhancing OCT imaging 100x faster with 3.5x improved contrast.

AIU-DDNC Publication

Publication of "AI Enabled Image Upscaler for Retinal Anomaly Detection with Dense Neural Computation" achieving 98.89% accuracy.

AI Reviews Publication

Comprehensive review of deep learning models for retinal disease classification, confirming 93-99% accuracy range.

Telemedicine Platform Evaluation

Publication of "Evaluation of OCT-AI Telemedicine Platform" showing 38% reduction in unnecessary referrals.

MAIA Integration Proposal

First proposal for integrating MAIA QFVC with P-GAN for optimized retinal imaging workflow.

Technical Details

P-GAN Architecture

Progressive GAN for OCT Enhancement

P-GAN utilizes a progressive growing architecture with the following components:

  • Generator: 8-layer U-Net with skip connections and attention mechanisms
  • Discriminator: PatchGAN with spectral normalization
  • Loss Function: Combination of adversarial loss, perceptual loss, and structural similarity index
  • Training: Progressive growing from 64×64 to 512×512 resolution
  • Dataset: 50,000 paired low-quality and high-quality OCT images

The model achieves its speed through optimized CUDA kernels and quantization-aware training, enabling deployment on consumer-grade GPUs in clinical settings.