Technical Implementation of Image Verification System

This article explains the implementation method of “Image Verify,” a real-time X-ray image quality verification system using AWS SageMaker AI. The system was built to reduce the 20% rejection rate of dental insurance claims due to image quality issues.

Technical Features:

  • Real-time Processing: Calculate the quality score immediately after capturing X-ray images (response time within 3 seconds)
  • Multi-model Architecture: Integrate multiple machine learning models to evaluate three dimensions: sharpness, alignment, and completeness
  • Scalability: Distributed processing architecture enables simultaneous processing at 40,000 locations

(Source: AWS Blogs)

Main Challenges and Solutions in System Design

During the development of Image Verify, it was necessary to satisfy five requirements simultaneously to ensure practicality in clinical settings.

Challenges and Countermeasures:

  1. Latency Control: Achieve response time within 3 seconds through GPU inference optimization
  2. Accuracy Assurance: Model training strategy to reduce false detection rate to 0.5% or less
  3. Cost Optimization: Maintain processing volume of 1.5 million images/week while reducing inference cost by 30%
  4. Global Deployment: Local edge computing configuration to ensure consistent performance in four regions

(Source: AWS Blogs)

Performance Metrics and Practicality

We introduce specific performance metrics obtained from actual operational data.

Key KPIs:

  • Cumulative number of processed images: 11 million (as of April 2026)
  • Weekly processing volume: 1.5 million (continuously increasing)
  • System utilization rate: Expansion to 40,000 locations (planned by 2026)
  • Reduction in insurance claim rejection rate: 55% decrease in rejections due to image quality issues

(Source: AWS Blogs)

Summary

  • Building a real-time image quality verification system using AWS SageMaker: Practical technical approach to ensure practicality in clinical settings
  • Achieving high scalability and low latency: Introduce optimization methods to achieve response time within 3 seconds
  • Improving cost efficiency: Adopt strategies to reduce inference cost by 30% while achieving large-scale processing
  • Enabling global deployment: Reference infrastructure design to ensure consistent performance in four regions
  • Improving insurance claim processing: Introduce a proven solution to reduce the rejection rate due to image quality issues by 55%