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Related Experiment Video

Updated: Aug 25, 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 in lung transplantation: Where are we?

Evgeni Mekov1, Viktoria Ilieva2

  • 1Department of Occupational Diseases, Faculty of Medicine, Medical University - Sofia, Sofia, Bulgaria.

Presse Medicale (Paris, France : 1983)
|October 17, 2022
PubMed
Summary

Machine learning (ML) offers advanced prediction for lung transplantation outcomes, improving upon traditional regression models. These ML tools can aid clinicians in crucial decisions like donor selection and rejection prediction.

Keywords:
Artificial intelligenceImagingLung transplantationMachine learningPathologyRandom forest

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Area of Science:

  • Medical research
  • Computer science
  • Transplantation science

Background:

  • Lung transplantation is a key treatment for end-stage respiratory failure.
  • Current outcome prediction relies on standard regression models.
  • Machine learning (ML) presents advanced predictive capabilities.

Purpose of the Study:

  • To explore the potential of supervised machine learning (ML) in lung transplantation.
  • To highlight ML's role as a decision-support tool for transplant clinicians.

Main Methods:

  • Review of current literature on ML applications in lung transplantation.
  • Analysis of ML's potential in areas like outcome prediction and clinical decision support.

Main Results:

  • ML techniques show promising results in predicting patient outcomes.
  • ML can assist in critical areas such as waiting list mortality, donor selection, and rejection prediction.
  • ML is advanced in imaging and pathology applications within lung transplantation.

Conclusions:

  • ML offers significant potential to enhance decision-making in lung transplantation.
  • Further research and adoption of ML are recommended for various aspects of lung transplant care.