The application of machine learning(ML)algorithms in various fields of hepatology is an issue of interest.However,we must be cautious with the results.In this letter,based on a published ML prediction model for acute ...The application of machine learning(ML)algorithms in various fields of hepatology is an issue of interest.However,we must be cautious with the results.In this letter,based on a published ML prediction model for acute kidney injury after liver surgery,we discuss some limitations of ML models and how they may be addressed in the future.Although the future faces significant challenges,it also holds a great potential.展开更多
The widespread uptake of different machine perfusion(MP)strategies for liver transplant has been driven by an effort to minimize graft injury.Damage to the cholangiocytes during the liver donation,preservation,or earl...The widespread uptake of different machine perfusion(MP)strategies for liver transplant has been driven by an effort to minimize graft injury.Damage to the cholangiocytes during the liver donation,preservation,or early posttransplant period may result in stricturing of the biliary tree and inadequate biliary drainage.This problem continues to trouble clinicians,and may have catastrophic consequences for the graft and patient.Ischemic injury,as a result of compromised hepatic artery flow,is a well-known cause of biliary strictures,sepsis,and graft failure.However,very similar lesions can appear with a patent hepatic artery and these are known as ischemic type biliary lesions(ITBL)that are attributed to microcirculatory dysfunction rather than main hepatic arterial compromise.Both the warm and cold ischemic period duration appear to influence the onset of ITBL.All of the commonly used MP techniques deliver oxygen to the graft cells,and therefore may minimize the cholangiocyte injury and subsequently reduce the incidence of ITBL.As clinical experience and published evidence grows for these modalities,the impact they have on ITBL rates is important to consider.In this review,the evidence for the three commonly used MP strategies(abdominal normothermic regional perfusion[A-NRP],hypothermic oxygenated perfusion[HOPE],and normothermic machine perfusion[NMP])for ITBL prevention has been critically reviewed.Inconsistencies with ITBL definitions used in trials,coupled with variations in techniques of MP,make interpretation challenging.Overall,the evidence suggests that both HOPE and A-NRP prevent ITBL in donated after circulatory death grafts compared to cold storage.The evidence for ITBL prevention in donor after brain death grafts with any MP technique is weak.展开更多
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 transplantation.Taki...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 transplantation.Taking into account the huge number of factors and variables involved in donor-recipient(DR)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 improve 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 experience.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 important.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 variables 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 deeplearning-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.展开更多
文摘The application of machine learning(ML)algorithms in various fields of hepatology is an issue of interest.However,we must be cautious with the results.In this letter,based on a published ML prediction model for acute kidney injury after liver surgery,we discuss some limitations of ML models and how they may be addressed in the future.Although the future faces significant challenges,it also holds a great potential.
基金funding received in the form of the Catherine Marie Enright research scholarship from the Royal Australasian College of Surgeons to support his program of research
文摘The widespread uptake of different machine perfusion(MP)strategies for liver transplant has been driven by an effort to minimize graft injury.Damage to the cholangiocytes during the liver donation,preservation,or early posttransplant period may result in stricturing of the biliary tree and inadequate biliary drainage.This problem continues to trouble clinicians,and may have catastrophic consequences for the graft and patient.Ischemic injury,as a result of compromised hepatic artery flow,is a well-known cause of biliary strictures,sepsis,and graft failure.However,very similar lesions can appear with a patent hepatic artery and these are known as ischemic type biliary lesions(ITBL)that are attributed to microcirculatory dysfunction rather than main hepatic arterial compromise.Both the warm and cold ischemic period duration appear to influence the onset of ITBL.All of the commonly used MP techniques deliver oxygen to the graft cells,and therefore may minimize the cholangiocyte injury and subsequently reduce the incidence of ITBL.As clinical experience and published evidence grows for these modalities,the impact they have on ITBL rates is important to consider.In this review,the evidence for the three commonly used MP strategies(abdominal normothermic regional perfusion[A-NRP],hypothermic oxygenated perfusion[HOPE],and normothermic machine perfusion[NMP])for ITBL prevention has been critically reviewed.Inconsistencies with ITBL definitions used in trials,coupled with variations in techniques of MP,make interpretation challenging.Overall,the evidence suggests that both HOPE and A-NRP prevent ITBL in donated after circulatory death grafts compared to cold storage.The evidence for ITBL prevention in donor after brain death grafts with any MP technique is weak.
基金supported by a grant from Mutua Madrile?a XVIII Convovatoria de ayudas a la investigación。
文摘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 transplantation.Taking into account the huge number of factors and variables involved in donor-recipient(DR)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 improve 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 experience.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 important.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 variables 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 deeplearning-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.
