Automated thermographic detection of blood vessels for DIEP flap reconstructive surgery.
Artificial intelligence
Breast reconstruction
Convolutional neural networks
DIEP flap
Dynamic infrared thermography
Machine learning
Perforator blood vessel
Reconstructive surgery
Thermography
Journal
International journal of computer assisted radiology and surgery
ISSN: 1861-6429
Titre abrégé: Int J Comput Assist Radiol Surg
Pays: Germany
ID NLM: 101499225
Informations de publication
Date de publication:
16 Jul 2024
16 Jul 2024
Historique:
received:
06
12
2023
accepted:
27
05
2024
medline:
17
7
2024
pubmed:
17
7
2024
entrez:
16
7
2024
Statut:
aheadofprint
Résumé
Inadequate 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. The 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. The 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. These 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.
Identifiants
pubmed: 39014178
doi: 10.1007/s11548-024-03199-8
pii: 10.1007/s11548-024-03199-8
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Fonds Wetenschappelijk Onderzoek
ID : G044420N
Organisme : Fonds Wetenschappelijk Onderzoek
ID : G0A9720N
Organisme : Fonds Wetenschappelijk Onderzoek
ID : 1SC0819N
Organisme : Fonds Wetenschappelijk Onderzoek
ID : T001723N
Informations de copyright
© 2024. CARS.
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