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Related Concept Videos

Tissue Transplantation01:24

Tissue Transplantation

864
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...
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An “All-laser” Endothelial Transplant
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AI-Driven Recipient Recognition System for Corneal Transplantation.

Ozer Sakin1, Mustafa Alper Selver2,3, Ozlem Barut Selver1,4,5,6,7

  • 1Ege University Faculty of Medicine, Ophthalmology Department, Izmir, TURKIYE.

Cornea
|December 10, 2025
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Summary

An artificial intelligence system was developed to improve corneal recipient selection. The AI system successfully identifies an average of 85% of suitable candidates, enhancing efficiency for eye banks.

Keywords:
artificial intelligencecorneal transplantationrecipient selection

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Area of Science:

  • Ophthalmology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Corneal transplantation relies on efficient recipient selection from large donor lists.
  • Current selection processes can be time-consuming and may benefit from technological assistance.

Purpose of the Study:

  • To develop an interactive program utilizing an artificial intelligence (AI)-based algorithm to simulate human operators in corneal recipient selection.
  • To enhance the efficiency and expediency of matching corneal donors with suitable recipients from extensive lists.

Main Methods:

  • Generated synthetic datasets of 1000 corneal recipients (21 features) and 50 donors (5 features).
  • Trained a multilayer perceptron machine learning model using operator-ranked recipient lists.
  • Compared AI-generated top 20 recipient candidates against operator selections for each donor.

Main Results:

  • The AI system endorsed 78% of the operator's top 11 initial selections and 67% of the top 13.
  • On average, the system identified 17 out of 20 suitable candidates, achieving approximately 85% concordance with operator choices.
  • The system demonstrated high agreement with human expert rankings.

Conclusions:

  • An adaptable interactive program simulating operator functions for corneal recipient selection has been developed.
  • The AI system can be trained in diverse eye bank settings, offering potential for widespread adoption.
  • This technology can streamline the corneal transplantation process by improving recipient selection efficiency.