Machine Learning Algorithms in Controlled Donation After Circulatory Death Under Normothermic Regional Perfusion: A Graft Survival Prediction Model.

Background: Several scores have been developed to stratify the risk of graft loss in controlled donation after circulatory death (cDCD). However, their performance is unsatisfactory in the Spanish population, where most cDCD livers are recovered using normothermic regional perfusion (NRP). Consequently, we explored the role of different machine learning-based classifiers as predictive models for graft survival.

Predicting waitlist dropout in hepatocellular carcinoma: a narrative review.

Background And Objective: Liver transplantation is the gold standard treatment for patients with hepatocellular carcinoma (HCC). Current allocation systems face a complex issue due to the imbalance between available organs and recipients. The prioritization of HCC patients remains controversial, leading to potential disparities in access to transplantation.

Machine learning in liver surgery: Benefits and pitfalls.

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.

A comprehensive evaluation of 80 consecutive robotic low anterior resections: impact of not mobilizing the splenic flexure alongside low-tie vascular ligation as a standardized technique.

Rectal cancer surgery represents challenges due to its location. To overcome them and minimize the risk of anastomosis-related complications, some technical maneuvers or even a diverting ileostomy may be required. One of these technical steps is the mobilization of the splenic flexure (SFM), especially in medium/low rectal cancer.

Transition from laparoscopic to robotic approach in rectal cancer: a single-center short-term analysis based on the learning curve.

As a novel procedure becomes more and more used, knowledge about its learning curve and its impact on outcomes is useful for future implementations. Our aim is (i) to identify the phases of the robotic rectal surgery learning process and assess the safety and oncological outcomes during that period, (ii) to compare the robotic rectal surgery learning phases outcomes with laparoscopic rectal resections performed before the implementation of the robotic surgery program. We performed a retrospective study, based on a prospectively maintained database, with methodological quality assessment by STROBE checklist.

Machine perfusion and the prevention of ischemic type biliary lesions following liver transplant: What is the evidence?

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.

Artificial intelligence and liver transplantation: Looking for the best donor-recipient pairing.

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 (D-R) matching, AI models may be well suited to improve organ allocation.