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Previewable Contract-Based On-Chain X-Ray Image Sharing Framework for Clinical Research

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 framew...

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Autores principales: Li, Megan Mun, Kuo, Tsung-Ting
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Author(s). Published by Elsevier B.V. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8479449/
https://www.ncbi.nlm.nih.gov/pubmed/34628257
http://dx.doi.org/10.1016/j.ijmedinf.2021.104599
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author Li, Megan Mun
Kuo, Tsung-Ting
author_facet Li, Megan Mun
Kuo, Tsung-Ting
author_sort Li, Megan Mun
collection PubMed
description 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. MAIN FINDINGS: 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. PRINCIPAL CONCLUSIONS: The findings of this study can guide future studies to generalize our framework to other forms of data to improve sharing and interoperability.
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spelling pubmed-84794492021-09-29 Previewable Contract-Based On-Chain X-Ray Image Sharing Framework for Clinical Research Li, Megan Mun Kuo, Tsung-Ting Int J Med Inform Article 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. MAIN FINDINGS: 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. PRINCIPAL CONCLUSIONS: The findings of this study can guide future studies to generalize our framework to other forms of data to improve sharing and interoperability. The Author(s). Published by Elsevier B.V. 2021-12 2021-09-28 /pmc/articles/PMC8479449/ /pubmed/34628257 http://dx.doi.org/10.1016/j.ijmedinf.2021.104599 Text en © 2021 The Author(s) Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Li, Megan Mun
Kuo, Tsung-Ting
Previewable Contract-Based On-Chain X-Ray Image Sharing Framework for Clinical Research
title Previewable Contract-Based On-Chain X-Ray Image Sharing Framework for Clinical Research
title_full Previewable Contract-Based On-Chain X-Ray Image Sharing Framework for Clinical Research
title_fullStr Previewable Contract-Based On-Chain X-Ray Image Sharing Framework for Clinical Research
title_full_unstemmed Previewable Contract-Based On-Chain X-Ray Image Sharing Framework for Clinical Research
title_short Previewable Contract-Based On-Chain X-Ray Image Sharing Framework for Clinical Research
title_sort previewable contract-based on-chain x-ray image sharing framework for clinical research
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8479449/
https://www.ncbi.nlm.nih.gov/pubmed/34628257
http://dx.doi.org/10.1016/j.ijmedinf.2021.104599
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