Evidence map›Paper›PMID 39014178›Full record

ArticleInternational journal of computer assisted radiology and surgery2024

Automated thermographic detection of blood vessels for DIEP flap reconstructive surgery.

Edgar Cardenas De La Hoz, Jan Verstockt, Simon Verspeek, Warre Clarys, Filip E F Thiessen, Thierry Tondu, Wiebren A A Tjalma, Gunther Steenackers, Steve Vanlanduit

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Article in International journal of computer assisted radiology and surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

9 authors.

Edgar Cardenas De La Hoz *InViLab Research Group, Faculty of Applied Engineering, University of Antwerp, Groenenborgerlaan 171, 2020, Wilrijk, Antwerp, Belgium. edgar.cardenas@uantwerpen.be.ORCID http://orcid.org/0000-0002-5098-3207
Jan Verstockt *InViLab Research Group, Faculty of Applied Engineering, University of Antwerp, Groenenborgerlaan 171, 2020, Wilrijk, Antwerp, Belgium.
Simon VerspeekInViLab Research Group, Faculty of Applied Engineering, University of Antwerp, Groenenborgerlaan 171, 2020, Wilrijk, Antwerp, Belgium.
Warre ClarysInViLab Research Group, Faculty of Applied Engineering, University of Antwerp, Groenenborgerlaan 171, 2020, Wilrijk, Antwerp, Belgium.
Filip E F ThiessenDepartment of Plastic, Reconstructive and Aesthetic Surgery, Multidisciplinary Breast Clinic, Antwerp University Hospital, Wilrijkstraat 10, 2650, Antwerp, Antwerp, Belgium.
Thierry TonduDepartment of Plastic, Reconstructive and Aesthetic Surgery, Multidisciplinary Breast Clinic, Antwerp University Hospital, Wilrijkstraat 10, 2650, Antwerp, Antwerp, Belgium.
Wiebren A A TjalmaGynaecological Oncology Unit, Department of Obstetrics and Gynaecology, Multidisciplinary Breast Clinic, Antwerp University Hospital, Wilrijkstraat 10, 2650, Antwerp, Antwerp, Belgium.
Gunther SteenackersInViLab Research Group, Faculty of Applied Engineering, University of Antwerp, Groenenborgerlaan 171, 2020, Wilrijk, Antwerp, Belgium.
Steve VanlanduitInViLab Research Group, Faculty of Applied Engineering, University of Antwerp, Groenenborgerlaan 171, 2020, Wilrijk, Antwerp, Belgium.

Funding

Fonds Wetenschappelijk Onderzoek 1SC0819NFonds Wetenschappelijk Onderzoek G044420NFonds Wetenschappelijk Onderzoek G0A9720NFonds Wetenschappelijk Onderzoek T001723N
6 · The paper itself

Abstract

purposeInadequate perfusion is the most common cause of partial flap loss in tissue transfer for post-mastectomy breast reconstruction. The current state-of-the-art uses computed tomography angiography (CTA) to locate the best perforators. Unfortunately, these techniques are expensive and time-consuming and not performed during surgery. Dynamic infrared thermography (DIRT) can offer a solution for these disadvantages.

methodsThe research presented couples thermographic examination during DIEP flap breast reconstruction with automatic segmentation approach using a convolutional neural network. Traditional segmentation techniques and annotations by surgeons are used to create automatic labels for the training.

resultsThe network used for image annotation is able to label in real-time on minimal hardware and the labels created can be used to locate and quantify perforator candidates for selection with a dice score accuracy of 0.8 after 2 min and 0.9 after 4 min.

conclusionsThese results allow for a computational system that can be used in place during surgery to improve surgical success. The ability to track and measure perforators and their perfused area allows for less subjective results and helps the surgeon to select the most suitable perforator for DIEP flap breast reconstruction.

Indexed as

MammaplastyPerforator FlapThermographyComputed Tomography AngiographyEpigastric ArteriesFemaleHumansNeural Networks, ComputerArtificial intelligenceBreast reconstructionConvolutional neural networksDIEP flapDynamic infrared thermographyMachine learningPerforator blood vesselReconstructive surgeryThermography

Identifiers

PMID39014178

What OpenQuestion holds

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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.