AIMC Topic: Lung Transplantation
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Advancing lung transplantation through machine learning and artificial intelligence.
Current opinion in pulmonary medicine
Apr 21, 2025
PURPOSE OF REVIEW: To explore the current applications of artificial intelligence and machine learning in lung transplantation, including outcome prediction, drug dosing, and the potential future uses and risks as the technology continues to evolve.
Development of a Machine Learning-Powered Optimized Lung Allocation System for Maximum Benefits in Lung Transplantation: A Korean National Data.
Journal of Korean medical science
Feb 24, 2025
BACKGROUND: An ideal lung allocation system should reduce waiting list deaths, improve transplant survival, and ensure equitable organ allocation. This study aimed to develop a novel lung allocation score (LAS) system, the MaxBenefit LAS, to maximize...
Developing approaches to incorporate donor-lung computed tomography images into machine learning models to predict severe primary graft dysfunction after lung transplantation.
American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons
Feb 7, 2025
Primary graft dysfunction (PGD) is a common complication after lung transplantation associated with poor outcomes. Although risk factors have been identified, the complex interactions between clinical variables affecting PGD risk are not well underst...
Identification and validation of biomarkers related to mitochondria during ex vivo lung perfusion for lung transplants based on machine learning algorithm.
Gene
Nov 15, 2024
BACKGROUND: Ex vivo lung perfusion (EVLP) is a critical strategy to rehabilitate marginal donor lungs, thereby increasing lung transplantation (LTx) rates. Ischemia-reperfusion (I/R) injury inevitably occurs during LTx. Exploring the common mechanism...
Deep learning-based approach for acquisition time reduction in ventilation SPECT in patients after lung transplantation.
Radiological physics and technology
Oct 23, 2024
We aimed to evaluate the image quality and diagnostic performance of chronic lung allograft dysfunction (CLAD) with lung ventilation single-photon emission computed tomography (SPECT) images acquired briefly using a convolutional neural network (CNN)...
Artificial intelligence-driven automated lung sizing from chest radiographs.
American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons
Aug 23, 2024
Lung size measurements play an important role in transplantation, as optimal donor-recipient size matching is necessary to ensure the best possible outcome. Although several strategies for size matching are currently used, all have limitations, and n...
Machine learning model predicts airway stenosis requiring clinical intervention in patients after lung transplantation: a retrospective case-controlled study.
BMC medical informatics and decision making
Aug 19, 2024
BACKGROUND: Patients with airway stenosis (AS) are associated with considerable morbidity and mortality after lung transplantation (LTx). This study aims to develop and validate machine learning (ML) models to predict AS requiring clinical interventi...
Novel dimensionality reduction method, Taelcore, enhances lung transplantation risk prediction.
Computers in biology and medicine
Jan 4, 2024
In this work, we present a new approach to predict the risk of acute cellular rejection (ACR) after lung transplantation by using machine learning algorithms, such as Multilayer Perceptron (MLP) or Autoencoder (AE), and combining them with topologica...
Machine Learning-Based Prognostic Model for Patients After Lung Transplantation.
JAMA network open
May 1, 2023
IMPORTANCE: Although numerous prognostic factors have been found for patients after lung transplantation (LTx) over the years, an accurate prognostic tool for LTx recipients remains unavailable.
Machine learning-based techniques to improve lung transplantation outcomes and complications: a systematic review.
BMC medical research methodology
Dec 23, 2022
BACKGROUND: Machine learning has been used to develop predictive models to support clinicians in making better and more reliable decisions. The high volume of collected data in the lung transplant process makes it possible to extract hidden patterns ...
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