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Defining weight trajectory after liver transplantation using generative artificial intelligence.

Alexis Kim1, Linxi Li2, Reem Hamraz3

  • 1Department of Internal Medicine, Virginia Commonwealth University Richmond, Virginia, USA.

Liver Transplantation : Official Publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society
|December 18, 2025
PubMed
Summary
This summary is machine-generated.

Generative AI identified two main weight gain patterns in liver transplant patients. Understanding these trajectories, influenced by factors like MASH cirrhosis and male gender, can improve patient care.

Keywords:
artificial intelligencebody-weight trajectoryliver cirrhosisliver transplantationrisk assessmentweight gain

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Area of Science:

  • Transplant medicine
  • Artificial Intelligence in healthcare
  • Biostatistics

Background:

  • Understanding weight changes after liver transplant (LT) is crucial for patient outcomes.
  • Current statistical methods may not fully capture complex weight trajectories.
  • Generative Artificial Intelligence (GenAI) offers a novel approach to model these patterns.

Purpose of the Study:

  • To apply GenAI for identifying and visualizing unobservable (latent) factors influencing weight trajectories in LT recipients.
  • To model individual weight changes up to 36 months post-LT.
  • To correlate identified weight patterns with clinical parameters.

Main Methods:

  • Modeled weight trajectories in 562 adult LT recipients using GenAI.
  • Transformed longitudinal weight data into cross-sectional vectors.
  • Conducted multivariate analyses to link latent factors with clinical data.

Main Results:

  • Identified two primary latent factors (LF1 and LF2) explaining 99% of weight variation post-LT.
  • LF1 characterized by early rapid weight flux and subsequent gradual gain.
  • LF2 showed distinct patterns of initial gain then loss, or initial loss then rapid gain.
  • MASH cirrhosis and male gender were significant predictors of weight gain.

Conclusions:

  • GenAI successfully identified key weight gain trajectories in LT recipients.
  • These identified patterns can enhance clinical risk stratification and management strategies.
  • Novel statistical approaches can uncover complex patient data patterns.