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Related Concept Videos

Orthogonal Trajectories01:26

Orthogonal Trajectories

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Orthogonal trajectories describe the geometric relationship between two families of curves that intersect each other at right angles. One illustrative case involves a family of parabolas that open sideways along the x-axis. These curves share a common shape but differ by a scaling parameter, resulting in a set of curves that all pass through the origin and widen at different rates.Determining Orthogonal TrajectoriesTo identify the orthogonal trajectories for these parabolas, the first step...
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In pediatric care, understanding the nuances of hepatic drug metabolism is crucial, as it significantly differs from that of adults. This divergence is primarily due to the developmental stage of drug-metabolizing enzymes, which affects how medications are processed in the body. In neonates, for instance, the activity of Phase I enzymes—critical for the initial breakdown of drugs—is markedly reduced, functioning at just 20–40% of the levels seen in adults. This reduction poses...
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Geriatric patients show significant variation in how their bodies process medications, which can change how effective and safe treatments are. The liver is the primary organ where drug metabolism occurs, involving two main types of chemical reactions: phase I and II. Phase I metabolism is driven by the cytochrome P450 enzyme system, which includes key types such as CYP3A, CYP2D6, and CYP2C9. Research indicates that while aging doesn't notably alter the levels or activity of these enzymes, it...
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Related Experiment Video

Updated: Jan 31, 2026

A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes
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Forecasting Waitlist Trajectories for Patients With Metabolic Dysfunction-Associated Steatohepatitis Cirrhosis: A

Gopika Punchhi1,2, Yingji Sun2, Eunice Tan2,3,4

  • 1Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.

Journal of Medical Internet Research
|January 29, 2026
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Summary

Deep learning models can predict liver transplant waitlist outcomes for patients with metabolic dysfunction-associated steatohepatitis cirrhosis. This approach improves forecasting of both transplant and mortality risks, aiding clinical decisions.

Keywords:
hepatic Diseasecirrhosisdeep learningliver diseaseliver transplantliver transplantationmetabolic dysfunctionmetabolic dysfunction-associated steatohepatitisneural networkpredictionpredictiverisk analysiswaitlist trajectory

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Area of Science:

  • Hepatology
  • Transplantation Medicine
  • Artificial Intelligence in Healthcare

Background:

  • Metabolic dysfunction-associated steatohepatitis (MASH) cirrhosis is a primary driver for liver transplantation (LT).
  • Current liver allocation systems (MELD-based) show limitations in predicting waitlist mortality for MASH patients.
  • Existing models fail to adequately account for competing risks of death and LT on the waitlist.

Purpose of the Study:

  • To develop and validate a deep learning model for forecasting waitlist trajectories in MASH cirrhosis patients.
  • To compare the predictive performance of deep learning against traditional models for competing risks.
  • To identify key factors influencing patient outcomes on the liver transplant waitlist.

Main Methods:

  • A deep learning competing risk model (DeepHit) was developed using data from 17,551 MASH cirrhosis patients.
  • Model performance was assessed using concordance index, Brier score, and a novel competing event coherence (CEC) score.
  • External validation was performed, and feature importance analysis identified key predictive variables.

Main Results:

  • DeepHit demonstrated superior CEC scores for predicting competing risks at multiple time points (1-12 months).
  • Random survival forests (RSF) showed higher concordance indices for death and transplant, except for death at 3 months.
  • MELD score, functional status, age, and blood type were identified as significant predictors of waitlist outcomes.

Conclusions:

  • Deep learning competing risk analysis offers a robust method for forecasting both death and transplant risks in MASH patients.
  • This approach can enhance clinical decision-making by highlighting critical prognostic factors.
  • The study underscores the potential of AI in optimizing liver transplant waitlist management.