Previewable Contract-Based On-Chain X-Ray Image Sharing Framework for Clinical Research.
Int J Med Inform
; 156: 104599, 2021 12.
Article
in English
| MEDLINE | ID: covidwho-1440101
ABSTRACT
BACKGROUND:
An image sharing framework is important to support downstream data analysis especially for pandemics like Coronavirus Disease 2019 (COVID-19). Current centralized image sharing frameworks become dysfunctional if any part of the framework fails. Existing decentralized image sharing frameworks do not store the images on the blockchain, thus the data themselves are not highly available, immutable, and provable. Meanwhile, storing images on the blockchain provides availability/immutability/provenance to the images, yet produces challenges such as large-image handling, high viewing latency while viewing images, and software inconsistency while storing/loading images.OBJECTIVE:
This study aims to store chest x-ray images using a blockchain-based framework to handle large images, improve viewing latency, and enhance software consistency. BASIC PROCEDURES We developed a splitting and merging function to handle large images, a feature that allows previewing an image earlier to improve viewing latency, and a smart contract to enhance software consistency. We used 920 publicly available images to evaluate the storing and loading methods through time measurements. MAINFINDINGS:
The blockchain network successfully shares large images up to 18 MB and supports smart contracts to provide code immutability, availability, and provenance. Applying the preview feature successfully shared images 93% faster than sharing images without the preview feature. PRINCIPALCONCLUSIONS:
The findings of this study can guide future studies to generalize our framework to other forms of data to improve sharing and interoperability.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Diagnostic Imaging
/
Blockchain
Type of study:
Experimental Studies
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
Int J Med Inform
Journal subject:
Medical Informatics
Year:
2021
Document Type:
Article
Affiliation country:
J.ijmedinf.2021.104599
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