Hyperspectral Imaging during Normothermic Machine Perfusion-A Functional Classification of Ex Vivo Kidneys Based on Convolutional Neural Networks

• Published in: Biomedicines Vol. 10; no. 2; p. 397
• Main Authors: Sommer, Florian, Sun, Bingrui, Fischer, Julian, Goldammer, Miriam, Thiele, Christine, Malberg, Hagen, Markgraf, Wenke
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 07.02.2022; MDPI
• Subjects: biomedical optical imaging; Cameras; Classification; hyperspectral imaging; Image processing; Ischemia; kidney; Kidney transplants; Kidneys; Laboratories; machine learning; Neural networks; normothermic machine perfusion; organ preservation; Oxygen saturation; Perfusion; Physiology; Software; Urine; United States > US; Germany; kidney; normothermic machine perfusion; convolutional neural network; residual neural network; classification; machine learning; function assessment; hyperspectral imaging; biomedical optical imaging; organ preservation
• ISSN: 2227-9059; 2227-9059
• DOI: 10.3390/biomedicines10020397

Facing an ongoing organ shortage in transplant medicine, strategies to increase the use of organs from marginal donors by objective organ assessment are being fostered. In this context, normothermic machine perfusion provides a platform for ex vivo organ evaluation during preservation. Consequently, analytical tools are emerging to determine organ quality. In this study, hyperspectral imaging (HSI) in the wavelength range of 550-995 nm was applied. Classification of 26 kidneys based on HSI was established using KidneyResNet, a convolutional neural network (CNN) based on the ResNet-18 architecture, to predict inulin clearance behavior. HSI preprocessing steps were implemented, including automated region of interest (ROI) selection, before executing the KidneyResNet algorithm. Training parameters and augmentation methods were investigated concerning their influence on the prediction. When classifying individual ROIs, the optimized KidneyResNet model achieved 84% and 62% accuracy in the validation and test set, respectively. With a majority decision on all ROIs of a kidney, the accuracy increased to 96% (validation set) and 100% (test set). These results demonstrate the feasibility of HSI in combination with KidneyResNet for non-invasive prediction of ex vivo kidney function. This knowledge of preoperative renal quality may support the organ acceptance decision.