Evidence map›Paper›PMID 34327624›Full record

ArticleJournal of digital imaging2021

A Conference-Friendly, Hands-on Introduction to Deep Learning for Radiology Trainees.

Walter F Wiggins, M Travis Caton, Kirti Magudia, Michael H Rosenthal, Katherine P Andriole

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of digital imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Walter F WigginsDepartment of Radiology, Brigham & Women's Hospital/Harvard Medical School, Boston, MA, USA. walter.wiggins@duke.edu.ORCID 0000-0002-0258-2708
M Travis CatonDepartment of Radiology, Brigham & Women's Hospital/Harvard Medical School, Boston, MA, USA.
Kirti MagudiaDepartment of Radiology, Brigham & Women's Hospital/Harvard Medical School, Boston, MA, USA.
Michael H RosenthalDepartment of Radiology, Brigham & Women's Hospital/Harvard Medical School, Boston, MA, USA.
Katherine P AndrioleDepartment of Radiology, Brigham & Women's Hospital/Harvard Medical School, Boston, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial or augmented intelligence, machine learning, and deep learning will be an increasingly important part of clinical practice for the next generation of radiologists. It is therefore critical that radiology residents develop a practical understanding of deep learning in medical imaging. Certain aspects of deep learning are not intuitive and may be better understood through hands-on experience; however, the technical requirements for setting up a programming and computing environment for deep learning can pose a high barrier to entry for individuals with limited experience in computer programming and limited access to GPU-accelerated computing. To address these concerns, we implemented an introductory module for deep learning in medical imaging within a self-contained, web-hosted development environment. Our initial experience established the feasibility of guiding radiology trainees through the module within a 45-min period typical of educational conferences.

Indexed as

Deep LearningRadiologyHumansMachine LearningRadiographyRadiologistsDeep learningMachine learningMedical educationMedical imaging

Identifiers

PMID34327624
PMCPMC8455745

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.