Evidence map›Paper›PMID 38558368›Full record

ArticleJournal of imaging informatics in medicine2024

A Guideline for Open-Source Tools to Make Medical Imaging Data Ready for Artificial Intelligence Applications: A Society of Imaging Informatics in Medicine (SIIM) Survey.

Sanaz Vahdati, Bardia Khosravi, Elham Mahmoudi, Kuan Zhang, Pouria Rouzrokh, Shahriar Faghani, Mana Moassefi, Aylin Tahmasebi, Katherine P Andriole, Peter Chang and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of imaging informatics in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

17 authors.

Sanaz VahdatiArtificial Intelligence Laboratory, Department of Radiology, Mayo Clinic, 200 1st Street, SW, Rochester, MN, 55905, USA.
Bardia KhosraviArtificial Intelligence Laboratory, Department of Radiology, Mayo Clinic, 200 1st Street, SW, Rochester, MN, 55905, USA.
Elham MahmoudiArtificial Intelligence Laboratory, Department of Radiology, Mayo Clinic, 200 1st Street, SW, Rochester, MN, 55905, USA.
Kuan ZhangArtificial Intelligence Laboratory, Department of Radiology, Mayo Clinic, 200 1st Street, SW, Rochester, MN, 55905, USA.
Pouria RouzrokhArtificial Intelligence Laboratory, Department of Radiology, Mayo Clinic, 200 1st Street, SW, Rochester, MN, 55905, USA.
Shahriar FaghaniArtificial Intelligence Laboratory, Department of Radiology, Mayo Clinic, 200 1st Street, SW, Rochester, MN, 55905, USA.
Mana MoassefiArtificial Intelligence Laboratory, Department of Radiology, Mayo Clinic, 200 1st Street, SW, Rochester, MN, 55905, USA.
Aylin TahmasebiDepartment of Radiology, Thomas Jefferson University, Philadelphia, PA, USA.
Katherine P AndrioleDepartment of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Peter ChangDepartment of Radiological Sciences, Irvine Medical Center, University of California, Orange, CA, USA.
Keyvan FarahaniCenter for Biomedical Informatics and Information Technology, National Cancer Institute, Bethesda, MD, USA.
Mona G FloresNVIDIA, Santa Clara, CA, USA.
Les FolioDiagnostic Imaging & Interventional Radiology Moffitt Cancer Center, Tampa, FL, USA.
Sina HoushmandDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
Maryellen L GigerDepartment of Radiology, The University of Chicago, Chicago, IL, USA.
Judy W GichoyaDepartment of Radiology, Emory University School of Medicine, Atlanta, GA, USA.
Bradley J EricksonArtificial Intelligence Laboratory, Department of Radiology, Mayo Clinic, 200 1st Street, SW, Rochester, MN, 55905, USA. bje@mayo.edu.ORCID http://orcid.org/0000-0001-7926-6095

Funding

Univ.of Calif., Irvine Cancer Center Support GrantP30CA062203 · NCI · UNIVERSITY OF CALIFORNIA-IRVINE · PI Melanie Funes · 1994 to 2026
$57.9M
NCI NIH HHS P30 CA062203
6 · The paper itself

Abstract

In recent years, the role of Artificial Intelligence (AI) in medical imaging has become increasingly prominent, with the majority of AI applications approved by the FDA being in imaging and radiology in 2023. The surge in AI model development to tackle clinical challenges underscores the necessity for preparing high-quality medical imaging data. Proper data preparation is crucial as it fosters the creation of standardized and reproducible AI models while minimizing biases. Data curation transforms raw data into a valuable, organized, and dependable resource and is a fundamental process to the success of machine learning and analytical projects. Considering the plethora of available tools for data curation in different stages, it is crucial to stay informed about the most relevant tools within specific research areas. In the current work, we propose a descriptive outline for different steps of data curation while we furnish compilations of tools collected from a survey applied among members of the Society of Imaging Informatics (SIIM) for each of these stages. This collection has the potential to enhance the decision-making process for researchers as they select the most appropriate tool for their specific tasks.

Indexed as

Artificial IntelligenceDiagnostic ImagingData CurationHumansMachine LearningMedical InformaticsSocieties, MedicalSurveys and QuestionnairesArtificial intelligenceData curationOpen sourceToolkits

Identifiers

PMID38558368
PMCPMC11522208

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.