Improving Diagnosis Through Digital Pathology: Proof-of-Concept Implementation Using Smart Contracts and Decentralized File Storage

BACKGROUND: Recent advancements in digital pathology resulting from advances in imaging and digitization have increased the convenience and usability of pathology for disease diagnosis, especially in oncology, urology, and gastroenteric diagnosis. However, despite the possibilities to include low-cost diagnosis and viable telemedicine, digital pathology is not yet accessible owing to expensive storage, data security requirements, and network bandwidth limitations to transfer high-resolution images and associated data. The increase in storage, transmission, and security complexity concerning data collection and diagnosis makes it even more challenging to use artificial intelligence algorithms for machine-assisted disease diagnosis. We designed and prototyped a digital pathology system that uses blockchain-based smart contracts using the nonfungible token (NFT) standard and the Interplanetary File System for data storage. Our design remediates shortcomings in the existing digital pathology systems infrastructure, which is centralized. The proposed design is extendable to other fields of medicine that require high-fidelity image and data storage. Our solution is implemented in data systems that can improve access quality of care and reduce the cost of access to specialized pathological diagnosis, reducing cycle times for diagnosis. OBJECTIVE: The main objectives of this study are to highlight the issues in digital pathology and suggest that a software architecture–based blockchain and the Interplanetary File System create a low-cost data storage and transmission technology. METHODS: We used the design science research method consisting of 6 stages to inform our design overall. We innovated over existing public-private designs for blockchains but using a 2-layered approach that separates actual file storage from metadata and data persistence. RESULTS: Here, we identified key challenges to adopting digital pathology, including challenges concerning long-term storage and the transmission of information. Next, using accepted frameworks in NFT-based intelligent contracts and recent innovations in distributed secure storage, we proposed a decentralized, secure, and privacy-preserving digital pathology system. Our design and prototype implementation using Solidity, web3.js, Ethereum, and node.js helped us address several challenges facing digital pathology. We demonstrated how our solution, which combines NFT smart contract standard with persistent decentralized file storage, solves most of the challenges of digital pathology and sets the stage for reducing costs and improving patient care and speed of diagnosis. CONCLUSIONS: We identified technical limitations that increase costs and reduce the mass adoption of digital pathology. We presented several design innovations using NFT decentralized storage standards to prototype a system. We also presented the implementation details of a unique security architecture for a digital pathology system. We illustrated how this design can overcome privacy, security, network-based storage, and data transmission limitations. We illustrated how improving these factors sets the stage for improving data quality and standardized application of machine learning and artificial intelligence to such data.