Evidence map›Paper›PMID 39567223›Full record

ReviewInternational wound journal2024

Non-invasive techniques for wound assessment: A comprehensive review.

Chunlin Zhao, Yuchen Guo, Lingli Li, Mei Yang

Abstract readReview
In one paragraph

Review in International wound journal, 2024. 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. Review
  2. Article
  3. Article
  4. Review
  5. Article
  6. Observational
  7. 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

4 authors.

Chunlin ZhaoDepartment of Thoracic Surgery, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, China.
Yuchen GuoWest China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, China.
Lingli LiWest China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, China.
Mei YangDepartment of Thoracic Surgery, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, China.

Funding

National Natural Science Foundation of China 52373237
6 · The paper itself

Abstract

Efficient wound assessment is essential for healthcare teams to facilitate prompt diagnosis, optimize treatment plans, reduce workload, and enhance patients' quality of life. In recent years, non-invasive techniques for aiding wound assessment, such as digital photography, 3D modelling, optical imaging, fluorescence and thermography, as well as artificial intelligence, have been gradually developed. This paper aims to review the various methods of measurement and diagnosis based on non-invasive wound imaging, and to summarize their application in wound monitoring and assessment. The goal is to provide a foundation and reference for future research on wound assessment.

Indexed as

Wounds and InjuriesArtificial IntelligenceHumansImaging, Three-DimensionalOptical ImagingPhotographyThermographyWound Healingimaging technologymobile medical deviceswound assessmentwound diagnosis

Identifiers

PMID39567223
PMCPMC11578670

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.