Deep Learning Analysis of In Vivo Hyperspectral Images for Automated Intraoperative Nerve Detection.
Fiche publication
Date publication
août 2021
Journal
Diagnostics (Basel, Switzerland)
Auteurs
Membres identifiés du Cancéropôle Est :
Pr MARESCAUX Jacques
Tous les auteurs :
Barberio M, Collins T, Bencteux V, Nkusi R, Felli E, Viola MG, Marescaux J, Hostettler A, Diana M
Lien Pubmed
Résumé
Nerves are critical structures that may be difficult to recognize during surgery. Inadvertent nerve injuries can have catastrophic consequences for the patient and lead to life-long pain and a reduced quality of life. Hyperspectral imaging (HSI) is a non-invasive technique combining photography with spectroscopy, allowing non-invasive intraoperative biological tissue property quantification. We show, for the first time, that HSI combined with deep learning allows nerves and other tissue types to be automatically recognized in in vivo hyperspectral images. An animal model was used, and eight anesthetized pigs underwent neck midline incisions, exposing several structures (nerve, artery, vein, muscle, fat, skin). State-of-the-art machine learning models were trained to recognize these tissue types in HSI data. The best model was a convolutional neural network (CNN), achieving an overall average sensitivity of 0.91 and a specificity of 1.0, validated with leave-one-patient-out cross-validation. For the nerve, the CNN achieved an average sensitivity of 0.76 and a specificity of 0.99. In conclusion, HSI combined with a CNN model is suitable for in vivo nerve recognition.
Mots clés
artificial intelligence, convolutional neural network, deep learning, hyperspectral imaging, intraoperative navigation tool, optical imaging, precision surgery, tissue recognition
Référence
Diagnostics (Basel). 2021 Aug 21;11(8):