Batchu S, Diaz MJ, Ladehoff L, Root K, Lucke-Wold B. Utilizing the Ethereum blockchain for retrieving and archiving augmented reality surgical navigation data.
EXPLORATION OF DRUG SCIENCE 2023;
1:55-63. [PMID:
36946757 PMCID:
PMC10026695 DOI:
10.37349/eds.2023.00005]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 01/17/2023] [Indexed: 03/06/2023]
Abstract
Aim
Conventional techniques to share and archive spinal imaging data raise issues with trust and security, with novel approaches being more greatly considered. Ethereum smart contracts present one such novel approach. Ethereum is an open-source platform that allows for the use of smart contracts. Smart contracts are packages of code that are self-executing and reside in the Ethereum state, defining conditions for programmed transactions. Though powerful, limited attempts have been made to showcase the clinical utility of such technologies, especially in the pre- and post-operative imaging arenas. Herein, we therefore aim to propose a proof-of-concept smart contract that stores intraoperative three-dimensional (3D) augmented reality surgical navigation (ARSN) data and was tested on a private, proof-of-authority network. To the author's best knowledge, the present study represents a first-use case of the Interplanetary File Storage protocol for storing and retrieving spine imaging smart contracts.
Methods
The content identifier hashes were stored inside the smart contracts while the interplanetary file system (IPFS) was used to efficiently store the image files. Insertion was achieved with four storage mappings, one for each of the following: fictitious patient data, specific diagnosis, patient identity document (ID), and Gertzbein grade. Inserted patient observations were then queried with wildcards. Insertion and retrieval times for different record volumes were collected.
Results
It took 276 milliseconds to insert 50 records and 713 milliseconds to insert 350 records. Inserting 50 records required 934 Megabyte (MB) of memory per insertion with patient data and imaging, while inserting 350 records required almost the same amount of memory per insertion. In a database of 350 records, the retrieval function needs about 1,026 MB to query a record with all three fields left blank, but only 970 MB to obtain the same observation from a database of 50 records.
Conclusions
The concept presented in this study exemplifies the clinical utility of smart contracts and off-chain data storage for efficient retrieval/insertion of ARSN data.
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