Abstract
Ischemia-reperfusion injury (IRI) has long been a challenge in organ transplantation and general surgery. Disorders characterized by IRI have a high mortality and morbidity rate, where delayed treatment is one of the causes, often due to the diffused symptoms of IRI. Therefore, new technologies for detecting and assessing IRI in an early stage are needed. The aim of this work is to improve the accuracy and precision of intraoperative assessment of small intestine viability and liver graft viability following IRI using various modalities. Specifically, we investigate the use of dielectric relaxation spectroscopy (DRS), electrical impedance spectroscopy (EIS), microscopic camera, and visible-near-infrared (VIS-NIR) spectroscopy. Our results demonstrate that dielectric properties can be used to differentiate between healthy, ischemic, and reperfused small intestine tissue. Using machine learning techniques, we can automate and improve the identification of viable and non-viable small intestinal segments compared to standard clinical methods. Microscopic images of the intestinal surface contain valuable information for distinguishing between different levels of IRI. In addition, the bioimpedance parameter Py measured using EIS may serve as an indicator for small intestine viability. Reflectance spectroscopy combined with partial least squares modeling shows high accuracy in predicting IRI in the small intestine.
List of papers
Paper I. J. Hou, R. Strand-Amundsen, C. Tronstad, T. I. Tønnessen, J. O. Høgetveit, and Ø. G. Martinsen. “Small intestinal viability assessment using dielectric relaxation spectroscopy and deep learning”. In: Scientific Reports. Vol. 12, no. 1 3279, (2022), DOI: 10.1038/s41598-022-07140-4. The article is included in the thesis. Also available at: https://doi.org/10.1038/s41598-022-07140-4 |
Paper II. J. Hou, R. Strand-Amundsen, C. Tronstad, J. O. Høgetveit, Ø. G. Martinsen, and T. I. Tønnessen. “Automatic Prediction of Ischemia-Reperfusion Injury of Small Intestine Using Convolutional Neural Networks: A Pilot Study”. In: Sensors. Vol. 21, No. 19, 6691, (2021), DOI: 10.3390/s21196691. The article is included in the thesis. Also available at: https://doi.org/10.3390/s21196691 |
Paper III. J. Hou, R. Strand-Amundsen, S. Hødnebø, T. I. Tønnessen, and J. O. Høgetveit. “Assessing ischemic injury in human intestine ex vivo with electrical impedance spectroscopy”. In: Journal of Electrical Bioimpedance. Vol. 12, No. 1, 82-88, (2021), DOI: 10.2478/joeb-2021-0011. The article is included in the thesis. Also available at: https://doi.org/10.2478/joeb-2021-0011 |
Paper IV. J. Hou, O. M. I. Liavåg, I. H. Færden, Ø. G. Martinsen, T. I. Tønnessen, P-D Line, M. Hagness, J. O. Høgetveit, S. E. Pischke, and R. Strand-Amundsen. “Utilization of dielectric properties for assessment of liver ischemia-reperfusion injury in vivo and during machine perfusion”. In Scientific Reports. Vol. 12, No. 1, 11183, 1-13, (2022), DOI: 10.1038/s41598-022-14817-3. The article is included in the thesis. Also available at: https://doi.org/10.1038/s41598-022-14817-3 |
Paper V. J. Hou, S. S. Ness, J. Tschudi, M. O’Farrell, R. Veddegjerde, Ø. G. Martinsen, T. I. Tønnessen, and R. Strand-Amundsen “Assessment of intestinal ischemiareperfusion injury using diffuse VIS-NIR spectroscopy and histology”. In: Sensors. Vol. 22, No. 23, 9111, (2022), DOI: 10.3390/s22239111. The article is included in the thesis. Also available at: https://doi.org/10.3390/s22239111 |