Reply: Machine learning models for NAFLD/NASH and cirrhosis diagnosis and staging: accuracy and routine variables are the success keys

Affiliations

¹ Arnold O. Beckman High School, Irvine, USA.
² Department of Medicine, Cedars Sinai Medical Center, Los Angeles, California, USA.
³ Houston Research Institute, Houston, Texas, USA.

Comment

Devon Chang et al. Hepatology. 2023.

. 2023 May 1;77(5):E105-E106.

doi: 10.1097/HEP.0000000000000211. Epub 2023 Jan 3.

¹ Arnold O. Beckman High School, Irvine, USA.
² Department of Medicine, Cedars Sinai Medical Center, Los Angeles, California, USA.
³ Houston Research Institute, Houston, Texas, USA.

No abstract available

Comment on

Machine learning models are superior to noninvasive tests in identifying clinically significant stages of NAFLD and NAFLD-related cirrhosis.
Chang D, Truong E, Mena EA, Pacheco F, Wong M, Guindi M, Todo TT, Noureddin N, Ayoub W, Yang JD, Kim IK, Kohli A, Alkhouri N, Harrison S, Noureddin M. Chang D, et al. Hepatology. 2023 Feb 1;77(2):546-557. doi: 10.1002/hep.32655. Epub 2022 Aug 9. Hepatology. 2023. PMID: 35809234
Letter to the editor: diagnosing fibrosis and cirrhosis in nonalcoholic fatty liver disease using machine learning models.
Feng Y, Peng B, Li Y, Sun L, Sun Y. Feng Y, et al. Hepatology. 2023 May 1;77(5):E103-E104. doi: 10.1097/HEP.0000000000000209. Epub 2023 Jan 3. Hepatology. 2023. PMID: 36645222 No abstract available.

1. Chang D, Truong E, Mena EA, Pacheco F, Wong M, Guindi M, et al. Machine learning models are superior to noninvasive tests in identifying clinically significant stages of NAFLD and NAFLD-related cirrhosis. Hepatology. 2023;77:546–557.
1. Dinani AM, Kowdley KV, Noureddin M. Application of artificial intelligence for diagnosis and risk stratification in NAFLD and NASH: the state of the art. Hepatology. 2021;74:2233–40.