Understanding Machine Learning Applications in Lung Transplantation: A Narrative Review.

ABSTRACT: Lung transplantation (LTx) offers life-saving therapy for patients with end-stage lung disease but remains limited by donor shortages, complex postoperative management and graft failure. Machine learning (ML) enables opportunities to address these challenges by identifying patterns in complex, high-dimensional data, thereby providing novel insights and improving outcomes. This review outlines ML studies in LTx and explains the methodologies. ML has demonstrated promising results in organ allocation and outcome prediction. Techniques such as support vector machines, and deep learning are useful in risk stratification, while methods like random forests improve interpretability and transfer learning supports model development in data-scarce settings. ML has a growing role in multi-omics data

SUBMITTER: Vercauteren B 

PROVIDER: S-EPMC12908661 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature