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Tissue Transplantation

Tissue Transplantation

Tissue transplantation is a significant medical procedure involving the transfer of cells, tissues, or organs from a donor to a recipient, with the primary aim of restoring lost functions. This procedure is crucial in treating a broad spectrum of diseases, including kidney diseases, liver failure, heart disease, and certain types of cancers.
The Biology of Tissue Transplantation
The biology of tissue transplantation hinges on the Major Histocompatibility Complex (MHC) molecules. These molecules...

Kidney Transplant I: Introduction

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A kidney transplant is a surgical approach that involves replacing a non-functioning kidney with a healthy one from a donor. This procedure is often a treatment option for end-stage renal disease (ESRD) patients. The method requires careful recipient selection, including evaluating various medical and psychosocial factors. These criteria vary between transplant centers but generally include assessments of the patient's overall health, adherence to medical recommendations, and lifestyle...

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Related Experiment Video

Updated: Dec 16, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Machine learning methods in organ transplantation.

David Guijo-Rubio¹, Pedro Antonio Gutiérrez, César Hervás-Martínez

¹Department of Computer Sciences and Numerical Analysis, University of Córdoba, Córdoba, Spain.

Current Opinion in Organ Transplantation

|July 4, 2020

Summary

This summary is machine-generated.

Machine learning (ML) is increasingly vital in organ transplantation for improving donor-recipient matching and patient outcomes. Proper data preprocessing is essential for developing robust ML models to support clinical decisions.

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Last Updated: Dec 16, 2025

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Area of Science:

Medical Informatics
Computational Biology
Transplantation Science

Background:

Machine learning (ML) has seen a surge in applications within organ transplantation over the past decade.
Recent advancements include deep learning models capable of handling large datasets.

Purpose of the Study:

To analyze the key applications of ML in organ transplantation.
To evaluate the benefits and drawbacks of using ML techniques in this field for clinical practitioners.

Main Methods:

Review of existing literature on ML applications in organ transplantation.
Analysis of proposed models, including multicenter and international cohorts.
Evaluation of deep learning approaches.

Main Results:

ML can enhance donor-recipient matching and improve standard scoring systems in transplantation.
Deep learning shows promise in managing extensive data for transplantation.

Conclusions:

ML offers significant potential to advance organ transplantation procedures.
High-quality, preprocessed data is crucial for developing reliable and fair ML decision-support systems.