Decision-making based on artificial intelligence(AI)methodology is increasingly present in all areas of modern medicine.In recent years,models based on deep-learning have begun to be used in organ trans-plantation.Taking into account the huge number of factors and variables involved in donor-recipient(D-R)matching,AI models may be well suited to improve organ allocation.AI-based models should provide two solutions:complement decision-making with current metrics based on logistic regression and im-prove their predictability.Hundreds of classifiers could be used to address this problem.However,not all of them are really useful for D-R pairing.Basically,in the decision to assign a given donor to a candidate in waiting list,a multitude of variables are handled,including donor,recipient,logistic and perioperative variables.Of these last two,some of them can be inferred indirectly from the team's previous experi-ence.Two groups of AI models have been used in the D-R matching:artificial neural networks(ANN)and random forest(RF).The former mimics the functional architecture of neurons,with input layers and output layers.The algorithms can be uni-or multi-objective.In general,ANNs can be used with large databases,where their generalizability is improved.However,they are models that are very sensitive to the quality of the databases and,in essence,they are black-box models in which all variables are impor-tant.Unfortunately,these models do not allow to know safely the weight of each variable.On the other hand,RF builds decision trees and works well with small cohorts.In addition,they can select top vari-ables as with logistic regression.However,they are not useful with large databases,due to the extreme number of decision trees that they would generate,making them impractical.Both ANN and RF allow a successful donor allocation in over 80％of D-R pairing,a number much higher than that obtained with the best statistical metrics such as model for end-stage liver disease,balance of risk score,and survival outcomes following liver transplantation scores.Many barriers need to be overcome before these deep-learning-based models can be included for D-R matching.The main one of them is the resistance of the clinicians to leave their own decision to autonomous computational models.

Javier Brice?o;Rafael Calleja;César Hervás

Unit of Liver Transplantation,Department of General Surgery,Hospital Universitario Reina Sofía,Córdoba,Spain;Maimónides Institute of Biomedical Research of Córdoba(IMIBIC),Córdoba,Spain;Department of Computer Sciences and Numerical Analysis,Universidad de Córdoba,Córdoba,Spain

国际肝胆胰疾病杂志(英文版)

[1]Javier Brice?o;Rafael Calleja;César Hervás-.Artificial intelligence and liver transplantation:Looking for the best donor-recipient pairing)[J].国际肝胆胰疾病杂志(英文版),2022(04):347-353

Artificial,intelligence,liver,transplantation,Looking,best,donor,recipient,pairing,Decision,making,artificial,methodology,increasingly,present,areas,modern,medicine,In,recent,years,models,deep,learning,have,begun,used,organ,Taking,into,account,huge,number,factors,variables,involved,matching,may,well,suited,allocation,should,provide,solutions,complement,decision,current,metrics,logistic,regression,their,predictability,Hundreds,classifiers,could,address,this,problem,However,not,them,really,useful,Basically,assign,given,candidate,waiting,list,multitude,handled,including,perioperative,Of,these,last,some,inferred,indirectly,from,team,previous,experi,Two,groups,been,neural,networks,random,forest,RF,former,mimics,functional,architecture,neurons,input,layers,output,algorithms,uni,objective,ANNs,large,databases,where,generalizability,improved,they,that,very,sensitive,quality,essence,black,box,which,impor,tant,Unfortunately,allow,know,safely,weight,each,On,other,builds,trees,small,cohorts,addition,select,top,due,extreme,would,generate,impractical,Both,successful,much,higher,than,obtained,statistical,such,end,stage,disease,balance,risk,survival,outcomes,following,scores,Many,barriers,need,overcome,before,included,main,one,resistance,clinicians,leave,own,autonomous,computational

0.531634