Evidence map›Paper›PMID 42857350›Full record

ArticleProceedings of SPIE--the International Society for Optical Engineering2024

Accurate burn wound categorization using an optimized spatial frequency domain imaging device and machine learning.

Chris Campbell, Gordon T Kennedy, Rebecca Rowland, Alexis M Fox, Earl Steward, Theresa Chin, Victor Joe, Anthony J Durkin

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of SPIE--the International Society for Optical Engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Chris CampbellBeckman Laser Institute and Medical Clinic, University of California Irvine, 1002 Health Sciences Road East, Irvine, CA 92697.
Gordon T KennedyBeckman Laser Institute and Medical Clinic, University of California Irvine, 1002 Health Sciences Road East, Irvine, CA 92697.
Rebecca RowlandBeckman Laser Institute and Medical Clinic, University of California Irvine, 1002 Health Sciences Road East, Irvine, CA 92697.
Alexis M FoxBeckman Laser Institute and Medical Clinic, University of California Irvine, 1002 Health Sciences Road East, Irvine, CA 92697.
Earl StewardBeckman Laser Institute and Medical Clinic, University of California Irvine, 1002 Health Sciences Road East, Irvine, CA 92697.
Theresa ChinUC Irvine Regional Burn Center, Department of Surgery, 333 City Boulevard West, Suite 705, Orange, CA 92868.
Victor JoeUC Irvine Regional Burn Center, Department of Surgery, 333 City Boulevard West, Suite 705, Orange, CA 92868.
Anthony J DurkinBeckman Laser Institute and Medical Clinic, University of California Irvine, 1002 Health Sciences Road East, Irvine, CA 92697.

Funding

Spatial Frequency Domain Imaging for Burn Wound Severity AssessmentR01GM108634 · NIGMS · UNIVERSITY OF CALIFORNIA-IRVINE · PI DURKIN, ANTHONY J · 2014 to 2023
$2.8M
NIGMS NIH HHS R01 GM108634
6 · The paper itself

Abstract

Effective burn wound management is informed by accurate severity assessment. Superficial partial-thickness burns do not require surgical intervention, while deep partial-thickness and full-thickness burn wounds necessitate skin grafting to minimize infection, contraction, and hypertrophic scarring. Visual-tactile wound assessment is subjective and error-prone, especially for inexperienced practitioners. A field- and hospital-deployable device, capable of quantifying both extent and severity of burns, could enable rapid, objective burn severity measurement with commensurate improvement in patient outcomes. Our group has previously shown that spatial frequency domain imaging (SFDI), a non-invasive, wide-field optical imaging technique, can accurately assess burn wound in a porcine model of controlled, graded burn severity

Indexed as

Burn severity assessmentMachine LearningSpatial Frequency Domain ImagingSupport Vector Machine

Identifiers

PMID42857350
PMCPMC13652300

What OpenQuestion holds

Textmetadata
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Registered trials

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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.