HYPerspectral Enhanced Reality (HYPER): a physiology-based surgical guidance tool - Barberio, Manuel; Longo, Fabio; Fiorillo, Claudio; Seeliger, Barbara; Mascagni, Pietro; Agnus, Vincent; Lindner, Veronique; Geny, Bernard; Charles, Anne-Laure; Gockel, Ines; Worreth, Marc; Saadi, Alend; et al. (Surgical endoscopy, 2020)
Background HSI is an optical technology allowing for a real-time, contrast-free snapshot of physiological tissue properties, including oxygenation. Hyperspectral imaging (HSI) has the potential to quantify the gastrointestinal perfusion intraoperatively. This experimental study evaluates the accuracy of HSI, in order to quantify bowel perfusion, and to obtain a superposition of the hyperspectral information onto real-time images. Methods In 6 pigs, 4 ischemic bowel loops were created (A, B, C, D) and imaged at set time points (from 5 to 360 min). A commercially available HSI system provided pseudo-color maps of the perfusion status (StO2, Near-InfraRed perfusion) and the tissue water index. An ad hoc software was developed to superimpose HSI information onto the live video, creating the HYPerspectral-based Enhanced Reality (HYPER). Seven regions of interest (ROIs) were identified in each bowel loop according to StO2 ranges, i.e., vascular (VASC proximal and distal), marginal vascular (MV proximal and distal), marginal ischemic (MI proximal and distal), and ischemic (ISCH). Local capillary lactates (LCL), reactive oxygen species (ROS), and histopathology were measured at the ROIs. A machine-learning-based prediction algorithm of LCL, based on the HSI-StO2%, was trained in the 6 pigs and tested on 5 additional animals. Results HSI parameters (StO2 and NIR) were congruent with LCL levels, ROS production, and histopathology damage scores at the ROIs discriminated by HYPER. The global mean error of LCL prediction was 1.18 ± 1.35 mmol/L. For StO2 values > 30%, the mean error was 0.3 ± 0.33. Conclusions HYPER imaging could precisely quantify the overtime perfusion changes in this bowel ischemia model.