Evidence map›Paper›PMID 34628257›Full record

ArticleInternational journal of medical informatics2021

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

Megan Mun Li, Tsung-Ting Kuo

Abstract read
In one paragraph

Article in International journal of medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Detecting model misconducts in decentralized healthcare federated learning.International journal of medical informatics · 2022
    Article
  8. Article
  9. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Megan Mun LiUniversity of California San Diego, La Jolla, CA, USA.
Tsung-Ting KuoUniversity of California San Diego, La Jolla, CA, USA. Electronic address: tskuo@health.ucsd.edu.

Funding

RADx-Rad Discoveries & Data: Consortium Coordination Center Program OrganizationU24LM013755 · NLM · YALE UNIVERSITY · PI ARONOFF-SPENCER, ELIAH S, OHNO-MACHADO, LUCILA · 2021 to 2023
$23.3M
San Diego Biomedical Informatics Education & Research (SABER)T15LM011271 · NLM · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SHAMIM NEMATI · 2012 to 2026
$9.7M
Protecting Privacy and Facilitating Shared Access of Clinical and Genetic Data of Special PopulationsR01HL136835 · NHLBI · YALE UNIVERSITY · PI OHNO-MACHADO, LUCILA · 2018 to 2022
$6.5M
iAGREE: A Multi-Center, Networked Patient Consent StudyR01HG011066 · NHGRI · YALE UNIVERSITY · PI OHNO-MACHADO, LUCILA · 2020 to 2024
$4.2M
Decentralized differentially-private methods for dynamic data release and analysisR01GM118609 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI JIANG, XIAOQIAN, OHNO-MACHADO, LUCILA · 2017 to 2020
$2.3M
BECKON - Block Estimate Chain: creating Knowledge ON demand & protecting privacyR00HG009680 · NHGRI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI KUO, TSUNG-TING · 2019 to 2021
$849k
NHGRI NIH HHS R00 HG009680NHGRI NIH HHS R01 HG011066NHLBI NIH HHS R01 HL136835NIGMS NIH HHS R01 GM118609NLM NIH HHS T15 LM011271NLM NIH HHS U24 LM013755
6 · The paper itself

Abstract

backgroundAn 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.

objectiveThis 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

findingsThe 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

conclusionsThe findings of this study can guide future studies to generalize our framework to other forms of data to improve sharing and interoperability.

Indexed as

BlockchainDiagnostic ImagingHumansSoftwareX-RaysBlockchainClinical ResearchImage Sharing Interoperability

Identifiers

PMID34628257
PMCPMC8479449

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.