O023 Artificial intelligence in photography of normothermic machine perfusion of livers

Abstract

Abstract Introduction Livers donated for use in transplantation are underutilised. A lack of clear guidelines regarding the visual assessment of their quality means many borderline grafts are needlessly discarded; macroscopic visual assessment by transplanting clinicians is subjective and inconsistent. Deep learning (DL), a form of artificial intelligence, has previously been used to aid medical image analysis. Normothermic machine perfusion (NMP) is often used to evaluate borderline livers for transplantation. This study aimed to develop an objective DL model to visually assess organ transplantability during NMP. Methods Five DL models were trained and tested on 100 images of donor livers, each labelled with scores from three transplant clinicians on steatosis, perfusion, and transplantability. Models were trained to classify liver transplantability through either image data and transplantability scoring data, or image data and all clinician scoring data. Model accuracy, specificity, and sensitivity were calculated. Evaluations of clinician scoring agreement were carried out using intraclass correlation coefficient (ICC) and Fleiss kappa. Results In the classification of transplantability, the highest performing models achieved training and testing accuracies of 64.3% and 76%, respectively. Sensitivity and specificity ranged between 44.1%–94.1% and 6.3%–75%, respectively. ICC and Fleiss kappa values indicated a fair-to moderate scoring agreement between clinicians. Conclusion The performance of DL image analysis in assessing liver quality during NMP has been modest. Visual assessment during NMP is more challenging than on the back table at organ retrieval. Broader, more varied data sets are required to maximise model performance. Take-home message Livers donated for use in transplantation are underutilised due to a lack of clear guidelines and the subjective nature of their visual assessment. Artificial intelligence can be used to consistently and objectively assess donated livers in order to improve their utilisation.