Machine learning algorithm improves the detection of NASH (NAS-based) and at-risk NASH: A development and validation study.
Lee J, Westphal M, Vali Y, Boursier J, Petta S, 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, Torstenson R, Karsdal M, Yunis C, Schattenberg JM, Schuppan D, Ratziu V, Brass C, Duffin K, Zwinderman K, Pavlides M, Anstee QM, Bossuyt PM; LITMUS investigators.
Lee J, et al.
Hepatology. 2023 Jul 1;78(1):258-271. doi: 10.1097/HEP.0000000000000364. Epub 2023 Mar 31.
Hepatology. 2023.
PMID: 36994719
BACKGROUND AND AIMS: Detecting NASH remains challenging, while at-risk NASH (steatohepatitis and F 2) tends to progress and is of interest for drug development and clinical application. ...Conditions of interest were the clinical trial definition of NASH (NAS 4;53%), at-ri …
BACKGROUND AND AIMS: Detecting NASH remains challenging, while at-risk NASH (steatohepatitis and F 2) tends to progress and is of int …