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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...

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Hoofdauteurs: Heather Mary-Kathleen Kosick, Chris McIntosh, Chinmay Bera, Mina Fakhriyehasl, Mohamed Shengir, Oyedele Adeyi, Leila Amiri, Giada Sebastiani, Kartik Jhaveri, Keyur Patel
Formaat: Artigo
Taal:Inglês
Gepubliceerd in: Nature Portfolio 2025-07-01
Reeks:Scientific Reports
Onderwerpen:
Steatosis
Artificial intelligence
Fibrosis
Liver biopsy
Hepatic decompensation
Online toegang:https://doi.org/10.1038/s41598-025-09288-1
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