Evidence map›Paper›PMID 35339253›Full record

ArticleSeminars in ultrasound, CT, and MR2022

A No-Math Primer on the Principles of Machine Learning for Radiologists.

Matthew D Lee, Mohammed Elsayed, Sumit Chopra, Yvonne W Lui

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Seminars in ultrasound, CT, and MR, 2022. 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. 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

4 authors.

Matthew D LeeDepartment of Radiology, NYU Grossman School of Medicine, New York, NY.
Mohammed ElsayedDepartment of Radiology, NYU Grossman School of Medicine, New York, NY.
Sumit ChopraDepartment of Radiology, NYU Grossman School of Medicine, New York, NY; Courant Institute of Mathematical Sciences, New York University, New York, NY.
Yvonne W LuiDepartment of Radiology, NYU Grossman School of Medicine, New York, NY. Electronic address: Yvonne.Lui@nyulangone.org.

Funding

TR&D 4: Revealing Microstructure: Biophysical modeling and validation for discovery and clinical careP41EB017183 · NIBIB · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Christopher M Collins · 2014 to 2026
$19.3M
NIBIB NIH HHS P41 EB017183
6 · The paper itself

Abstract

Machine learning is becoming increasingly important in both research and clinical applications in radiology due to recent technological developments, particularly in deep learning. As these technologies are translated toward clinical practice, there is a need for radiologists and radiology trainees to understand the basic principles behind them. This primer provides an accessible introduction to the vocabulary and concepts that are central to machine learning and relevant to the radiologist.

Indexed as

Machine LearningRadiologyHumansRadiologists

Identifiers

PMID35339253
PMCPMC9363000

What OpenQuestion holds

Textmetadata
LicenceTDM
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.