Evidence map›Paper›PMID 33219279›Full record

ArticleScientific reports2020

Non-contact monitoring of the depth temperature profile for medical laser scanning technologies.

Jure Kosir, Daniele Vella, Matija Jezersek

Abstract read
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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.

Jure KosirFaculty of Mechanical Engineering, University of Ljubljana, Askerceva 6, Ljubljana, Slovenia. jure.kosir@fs.uni-lj.si.
Daniele VellaFaculty of Mechanical Engineering, University of Ljubljana, Askerceva 6, Ljubljana, Slovenia.
Matija JezersekFaculty of Mechanical Engineering, University of Ljubljana, Askerceva 6, Ljubljana, Slovenia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Medical treatments such as high-intensity focused ultrasound, hyperthermic laser lipolysis or radiofrequency are employed as a minimally invasive alternatives for targeted tissue therapies. The increased temperature of the tissue triggers various thermal effects and leads to an unavoidable damage. As targeted tissues are generally located below the surface, various approaches are utilized to prevent skin layers from overheating and irreparable thermal damages. These procedures are often accompanied by cooling systems and protective layers accounting for a non-trivial detection of the subsurface temperature peak. Here, we show a temperature peak estimation method based on infrared thermography recording of the surface temperature evolution coupled with a thermal-diffusion-based model and a time-dependent data matching algorithm. The performance of the newly developed method was further showcased by employing hyperthermic laser lipolysis on an ex-vivo porcine fat tissue. Deviations of the estimated peak temperature remained below 1 °C, as validated by simultaneous measurement of depth temperature field within the tissue. Reconstruction of the depth profile shows a good reproducibility of the real temperature distribution with a small deviation of the peak temperature position. A thermal camera in combination with the time-dependent matching bears the scope for non-contact monitoring of the depth temperature profile as fast as 30 s. The latest demand for miniaturization of thermal cameras provides the possibility to embed the model in portable thermal scanners or medical laser technologies for improving safety and efficiency.

Identifiers

PMID33219279
PMCPMC7679450

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.