Artificial Intelligence and Liver Transplant: Predicting Survival of Individual Grafts

BACKGROUND: The demand for liver transplantation far outstrips the supply of deceased donor organs, and so listing and allocation decisions aim to maximise utility. Most existing methods for predicting transplant outcomes utilise basic methods such as regression modelling - newer artificial intelligence techniques have the potential to improve predictive accuracy.

AIMS: To systematically review studies predicting graft outcomes following deceased liver transplantation using Artificial Intelligence (AI) techniques and comparing these to linear regression and standard predictive modelling (donor risk index, DRI; Model for end-stage liver disease, MELD; survival outcome following liver transplantation, SOFT).

METHODS: A systematic review was performed. PubMed, Cochrane, MEDLINE, Science Direct, Springer Link, Elsevier, and reference lists were analysed for appropriate inclusion.

RESULTS: A total of 52 papers were reviewed for inclusion. Of these papers, 9 met the inclusion criteria, reporting outcomes from 18,771 liver transplants. Artificial neural networks (ANN) were the most commonly utilised methodology, being reported in 7 studies. Only two studies directly compared Machine Learning (ML) techniques to liver scoring modalities (i.e. DRI, SOFT, BAR). Both of these studies showed better prediction of individual organ survival with the optimal ANN model reporting AUC ROC 0.82 compared with BAR: 0.62 and SOFT: 0.57; and the other ANN model showing an AUC ROC: 0.84 compared to DRI: 0.68 and SOFT: 0.64.

CONCLUSION: AI techniques can provide high accuracy in predicting graft survival based on donors and recipient variables. When compared to standard techniques, AI methods are dynamic - able to be trained and validated within every population. However, the high accuracy of AI may come at a cost of losing explainability (to patients and clinicians) on how the technology works.