Preoperative Function Assessment of Ex Vivo Kidneys with Supervised Machine Learning Based on Blood and Urine Markers Measured during Normothermic Machine Perfusion

Present and Future Applications of Artificial Intelligence in Kidney Transplantation

Artificial intelligence (AI) has a wide and increasing range of applications across various sectors. In medicine, AI has already made an impact in numerous fields, rapidly transforming healthcare delivery through its growing applications in diagnosis, treatment and overall patient care. Equally, AI is swiftly and essentially transforming the landscape of kidney transplantation (KT), offering innovative solutions for longstanding problems that have eluded resolution through traditional approaches outside its spectrum. The purpose of this review is to explore the present and future applications of artificial intelligence in KT, with a focus on pre-transplant evaluation, surgical assistance, outcomes and post-transplant care. We discuss its great potential and the inevitable limitations that accompany these technologies. We conclude that by fostering collaboration between AI technologies and medical practitioners, we can pave the way for a future where advanced, personalised care becomes the standard in KT and beyond.

Assessment of hemodynamic and blood parameters that may reflect macroscopic quality of porcine kidneys during normothermic machine perfusion using whole blood

PURPOSE

Using ex vivo normothermic machine perfusion (NMP) with whole blood we assessed marginal porcine kidneys under reperfusion. The aim was to link measureable machine and clinical blood parameters with the currently used visual assessment. This could serve as a baseline for a standardized evaluation score to identify potentially transplantable kidneys in the future.

METHODS

Kidneys and autologous whole blood were procured from slaughterhouse pigs (n = 33) and were perfused for 4 h using NMP. The hemodynamic parameters arterial pressure (AP), renal blood flow (RBF) and intrarenal resistance (IRR) were measured. Activity of aspartate transaminase (AST), gamma-glutamyltransferase (GGT), alkaline phosphatase (ALP), lactate dehydrogenase (LDH) and lactate were assessed in blood at 0/1/2/4 h. Kidneys were grouped into "potentially transplantable" (PT) or "not transplantable" (NT) based on their overall macroscopic appearance after NMP by an experienced physician.

RESULTS

PT-kidneys (n = 20) had a significantly lower IRR and higher RBF than NT-kidneys (n = 13). GGT, ALP and LDH did not differ significantly, but at 4 h, AST was significantly higher in PT-kidneys compared to NT-kidneys. Lactate levels kept increasing during NMP in NT-kidneys and were significantly higher at 1/2/4 h than in PT-kidneys.

CONCLUSION

The immediately assessed macroscopic aspects of examined kidneys correlated with hemodynamic parameters, increased lactate and lower AST in this study. In the future, NMP with whole blood could be a useful tool to extend the donor pool by allowing the assessment of otherwise unknown characteristics of marginal kidneys before transplantation.

Pre-transplant kidney quality evaluation using photoacoustic imaging during normothermic machine perfusion

Due to the shortage of kidneys donated for transplantation, surgeons are forced to use the organs with an elevated risk of poor function or even failure. Although the existing methods for pre-transplant quality evaluation have been validated over decades in population cohort studies across the world, new methods are needed as long as delayed graft function or failure in a kidney transplant occurs. In this study, we explored the potential of utilizing photoacoustic (PA) imaging during normothermic machine perfusion (NMP) as a means of evaluating kidney quality. We closely monitored twenty-two porcine kidneys using 3D PA imaging during a two-hour NMP session. Based on biochemical analyses of perfusate and produced urine, the kidneys were categorized into 'non-functional' and 'functional' groups. Our primary focus was to quantify oxygenation (sO2) within the kidney cortical layer of depths 2 mm, 4 mm, and 6 mm using two-wavelength PA imaging. Next, receiver operating characteristic (ROC) analysis was performed to determine an optimal cortical layer depth and time point for the quantification of sO2 to discriminate between functional and non-functional organs. Finally, for each depth, we assessed the correlation between sO2 and creatinine clearance (CrCl), oxygen consumption (VO2), and renal blood flow (RBF). We found that hypoxia of the renal cortex is associated with poor renal function. In addition, the determination of sO2 within the 2 mm depth of the renal cortex after 30 min of NMP effectively distinguishes between functional and non-functional kidneys. The non-functional kidneys can be detected with the sensitivity and specificity of 80% and 85% respectively, using the cut-off point of sO2 < 39%. Oxygenation significantly correlates with RBF and VO2 in all kidneys. In functional kidneys, sO2 correlated with CrCl, which is not the case for non-functional kidneys. We conclude that the presented technique has a high potential for supporting organ selection for kidney transplantation.