Evidence map›Paper›PMID 39170189›Full record

ArticleHeliyon2024

Automatic detection of pleural line and lung sliding in lung ultrasonography using convolutional neural networks.

Takeyoshi Uchida, Yukimi Tanaka, Akihiro Suzuki

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. 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

3 authors.

Takeyoshi UchidaMaterial Strength Standards Group, Research Institute for Engineering Measurement, National Metrology Institute of Japan, National Institute of Advanced Industrial Science and Technology, Central 3, 1-1-1 Umezono, Tsukuba, 305-8563, Japan.
Yukimi TanakaMaterial Strength Standards Group, Research Institute for Engineering Measurement, National Metrology Institute of Japan, National Institute of Advanced Industrial Science and Technology, Central 3, 1-1-1 Umezono, Tsukuba, 305-8563, Japan.
Akihiro SuzukiDepartment of Anesthesiology and Critical Care Medicine, Jichi Medical University, Yakushiji, Shimotsuke-shi, Tochigi, 329-0498, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Lung ultrasonography (LUS) is a valuable diagnostic tool, but there is a shortage of LUS experts with extensive knowledge and significant experience in the field. Convolutional neural networks (CNNs) have the potential to mitigate this issue by facilitating computer-aided diagnosis. Methods: We propose computer-aided system by a CNN-based method for LUS diagnosis. As the first consideration, we investigated pleural line and lung sliding. The pleural line indicates the position of pleura in an ultrasound image, and LUS is performed after first confirming the position of pleural line. Lung sliding defined as the movement of the pleural line, and the absence of this feature is associated with pneumothorax. Results: Our proposed method accurately detected pleural line and lung sliding, demonstrating its potential to provide valuable diagnostic information on lung lesions.

Indexed as

Convolutional neural networkLung lesionsLung slidingLung ultrasonographyPleural linePneumothoraxUltrasound diagnosis

Identifiers

PMID39170189
PMCPMC11336331

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.