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Murine Corneal Transplantation: A Model to Study the Most Common Form of Solid Organ Transplantation

Murine Corneal Transplantation: A Model to Study the Most Common Form of Solid Organ Transplantation

Published on: November 17, 2014

Machine learning in solid organ transplantation: Charting the evolving landscape.

Badi Rawashdeh¹, Haneen Al-Abdallat², Emre Arpali³

¹Division of Transplant Surgery, Medical College of Wisconsin, Milwaukee, WI 53202, United States. brawashdeh@mcw.edu.

World Journal of Transplantation

|March 19, 2025

Summary

This summary is machine-generated.

Machine learning (ML) is increasingly used in solid organ transplantation research, with a notable rise in publications since 2018. This trend highlights ML's potential to improve patient outcomes and advance transplant medicine.

Keywords:

Artificial Intelligence Bibliometric analysis Machine learning Solid organ transplantation

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Area of Science:

Transplantation Medicine
Artificial Intelligence
Data Science

Background:

Machine learning (ML) offers significant advancements in healthcare, particularly in solid organ transplantation.
ML aids in donor-recipient matching, post-transplant monitoring, and patient care through data analysis and outcome prediction.

Purpose of the Study:

To perform a comprehensive bibliometric analysis of research on ML in transplantation.
To identify current research trends and their implications in this field.

Main Methods:

A systematic search of the Web of Science database was conducted using ML and transplantation keywords.
Bibliometric analysis of 427 relevant articles was performed using VOS viewer software.
Key variables analyzed included publication counts, citations, contributing countries, and institutions.

Main Results:

Publication volume in ML for transplantation has surged, particularly after 2018.
The United States is the leading contributor with 209 publications.
Key research themes include patient survival, mortality, outcomes, allocation, and risk assessment.

Conclusions:

The increasing number of publications underscores the growing importance of ML in solid organ transplantation research.
ML holds significant potential to transform transplant practices and improve patient outcomes.
Enhanced collaboration among leading contributors can accelerate progress in this interdisciplinary field.