Automatic optical biopsy for colorectal cancer using hyperspectral imaging and artificial neural networks.
Fiche publication
Date publication
août 2022
Journal
Surgical endoscopy
Auteurs
Membres identifiés du Cancéropôle Est :
Pr MARESCAUX Jacques
Tous les auteurs :
Collins T, Bencteux V, Benedicenti S, Moretti V, Mita MT, Barbieri V, Rubichi F, Altamura A, Giaracuni G, Marescaux J, Hostettler A, Diana M, Viola MG, Barberio M
Lien Pubmed
Résumé
Intraoperative identification of cancerous tissue is fundamental during oncological surgical or endoscopic procedures. This relies on visual assessment supported by histopathological evaluation, implying a longer operative time. Hyperspectral imaging (HSI), a contrast-free and contactless imaging technology, provides spatially resolved spectroscopic analysis, with the potential to differentiate tissue at a cellular level. However, HSI produces "big data", which is impossible to directly interpret by clinicians. We hypothesize that advanced machine learning algorithms (convolutional neural networks-CNNs) can accurately detect colorectal cancer in HSI data.
Mots clés
Artificial intelligence, Colorectal cancer, Convolutional neural network, Deep learning, Hyperspectral, Spectral
Référence
Surg Endosc. 2022 08 25;: