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Date publication
août 2022
Journal
Surgical endoscopy
Auteurs
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;:
