A Novel Deep Learning Model as a Donor-Recipient Matching Tool to Predict Survival after Liver Transplantation.
Nikolaus BörnerMarkus Bo SchoenbergPhilipp PöschkeChristian HeiligerSven-Niclas JacobDominik KochBenedikt PöllmannMoritz DrefsDionysios KoliogiannisChristian BöhmKonrad W KarczJens WernerMarkus GubaPublished in: Journal of clinical medicine (2022)
With the achieved results, the network serves as a reliable tool to predict survival. It adds new insight into the potential of deep learning to assist medical decisions. Especially in the field of transplantation, an AUC Score of 94% is very valuable. This neuronal network is unique as it utilizes transparent and easily interpretable data to predict the outcome after liver transplantation. Further validation must be performed prior to utilization in a clinical context.