Machine learning models using non-invasive tests & B-mode ultrasound to predict liver-related outcomes in metabolic dysfunction-associated steatotic liver disease

Machine learning models using non-invasive tests & B-mode ultrasound to predict liver-related outcomes in metabolic dysfunction-associated steatotic liver disease

Abstract Advanced metabolic-dysfunction-associated steatotic liver disease (MASLD) fibrosis (F3-4) predicts liver-related outcomes. Serum and elastography-based non-invasive tests (NIT) cannot yet reliably predict MASLD outcomes. The role of B-mode ultrasound (US) for outcome prediction is not yet k...

Full description

Saved in:
Bibliographic Details
Main Authors: Heather Mary-Kathleen Kosick, Chris McIntosh, Chinmay Bera, Mina Fakhriyehasl, Mohamed Shengir, Oyedele Adeyi, Leila Amiri, Giada Sebastiani, Kartik Jhaveri, Keyur Patel
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Steatosis
Artificial intelligence
Fibrosis
Liver biopsy
Hepatic decompensation
Online Access:https://doi.org/10.1038/s41598-025-09288-1
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1038/s41598-025-09288-1

Similar Items