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This study introduces a blockchain framework for sharing large medical images, improving data availability and reducing viewing times by 93% using a preview feature. This enhances secure image analysis for pandemics.

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Area of Science:

  • Medical Informatics
  • Blockchain Technology
  • Data Management

Background:

  • Centralized image sharing frameworks are vulnerable to single points of failure, impacting critical data analysis during pandemics like COVID-19.
  • Existing decentralized systems lack on-chain storage, compromising image availability, immutability, and verifiability.
  • Storing images directly on the blockchain presents challenges in handling large files, latency, and software consistency.

Purpose of the Study:

  • To develop and evaluate a blockchain-based framework for storing and sharing chest x-ray images.
  • To address challenges of large image handling, viewing latency, and software consistency in blockchain image sharing.
  • To enhance the availability, immutability, and provenance of medical image data.

Main Methods:

  • Developed a splitting and merging function for efficient large image handling on the blockchain.
  • Implemented a preview feature to reduce image viewing latency.
  • Utilized smart contracts to ensure software consistency, immutability, availability, and provenance.
  • Evaluated performance using 920 public chest x-ray images, measuring storage and loading times.

Main Results:

  • The blockchain framework successfully managed and shared large images up to 18 MB.
  • Smart contracts provided code immutability, availability, and provenance for shared images.
  • The preview feature accelerated image sharing by 93% compared to methods without it.

Conclusions:

  • The developed blockchain framework effectively addresses challenges in sharing large medical images.
  • The framework enhances data security, availability, and analysis efficiency.
  • Findings offer a foundation for generalizing blockchain solutions to other data types, improving interoperability.