Improving Diagnosis Through Digital Pathology: Proof-of-Concept Implementation Using Smart Contracts and...

Area of Science:

• Biomedical Informatics
• Computer Science

Background:

• Digital pathology advancements offer improved disease diagnosis but face barriers like high storage costs, data security, and network limitations.
• These infrastructure challenges hinder the adoption of artificial intelligence (AI) for machine-assisted diagnosis in digital pathology.
• Current centralized systems struggle with the increasing complexity of data collection, storage, and transmission.

Purpose of the Study:

• To address the limitations of current digital pathology systems.
• To propose a blockchain and Interplanetary File System-based architecture for low-cost data storage and transmission.
• To enhance the accessibility and usability of digital pathology for improved healthcare.

Main Methods:

• Employed a 6-stage design science research methodology.
• Developed an innovative two-layered blockchain approach separating file storage from metadata and data persistence.
• Utilized non-fungible token (NFT) standards and the Interplanetary File System for decentralized data management.

Main Results:

• Designed and prototyped a decentralized, secure, and privacy-preserving digital pathology system.
• Demonstrated how combining NFT smart contracts with decentralized storage addresses key challenges in digital pathology.
• The prototype implementation, using Solidity, web3.js, Ethereum, and node.js, showed potential for cost reduction and improved diagnostic speed.

Conclusions:

• Identified technical limitations hindering digital pathology's mass adoption and cost-effectiveness.
• Proposed a novel system leveraging NFT decentralized storage standards and a unique security architecture.
• The design overcomes privacy, security, storage, and transmission limitations, paving the way for enhanced data quality and AI application.