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Machine learning algorithm improves detection of NASH (NAS-based) and at-risk NASH: a development and validation study

Lookup NU author(s): Kristy Wonders, Dr Dina Tiniakos, Professor Pierre Bedossa, Professor Cliff Brass, Professor Quentin AnsteeORCiD



This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).

Publication metadata

Author(s): Lee J, Westphal M, Vali Y, Boursier J, Ostroff R, Alexander L, Chen Y, Fournier C, Geier A, Francque S, Wonders K, Tiniakos D, Bedossa P, Allison M, Papatheodoridis G, Cortez-Pinto H, Pais R, Dufour JF, Leeming DJ, Harrison S, Cobbold J, Holleboom AG, Yki-Järvinen H, Crespo J, Ekstedt M, Aithal GP, Bugianesi E, Romero-Gomez M, Karsdal M, Yunis C, Schattenberg JM, Schuppan D, Ratziu V, Brass C, Duffin K, Zwinderman K, Pavlides M, Anstee QM, Bossuyt PM

Publication type: Article

Publication status: Published

Journal: Hepatology

Year: 2023

Volume: 78

Issue: 1

Pages: 258-271

Online publication date: 01/07/2023

Acceptance date: 31/12/2022

Date deposited: 19/12/2023

ISSN (print): 0270-9139

ISSN (electronic): 1527-3350

Publisher: Wolters Kluwer Health


DOI: 10.1097/HEP.0000000000000364


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Funder referenceFunder name
777377European Commission