Automatic Liver Viability Scoring with Deep Learning and Hyperspectral Imaging
Éric Felli, Éric Felli, Mahdi Al‐Taher, Toby Collins, Richard Nkusi, Emanuele Felli, Emanuele Felli, Andrea Baiocchini, Véronique Lindner, Cindy Vincent, Manuel Barberio, Bernard Gény, Giuseppe Maria Ettorre, Alexandre Hostettler, Didier Mutter, Sylvain Gioux, Catherine Schuster, Jacques Marescaux, Jordi Gracia‐Sancho, Michèle Diana
Abstract
Hyperspectral imaging (HSI) is a non-invasive imaging modality already applied to evaluate hepatic oxygenation and to discriminate different models of hepatic ischemia. Nevertheless, the ability of HSI to detect and predict the reperfusion damage intraoperatively was not yet assessed. Hypoxia caused by hepatic artery occlusion (HAO) in the liver brings about dreadful vascular complications known as ischemia-reperfusion injury (IRI). Here, we show the evaluation of liver viability in an HAO model with an artificial intelligence-based analysis of HSI. We have combined the potential of HSI to extract quantitative optical tissue properties with a deep learning-based model using convolutional neural networks. The artificial intelligence (AI) score of liver viability showed a significant correlation with capillary lactate from the liver surface (r = −0.78, p = 0.0320) and Suzuki’s score (r = −0.96, p = 0.0012). CD31 immunostaining confirmed the microvascular damage accordingly with the AI score. Our results ultimately show the potential of an HSI-AI-based analysis to predict liver viability, thereby prompting for intraoperative tool development to explore its application in a clinical setting.