LEARN algorithm: a novel option for predicting non-alcoholic steatohepatitis.

Autor:	Li G; MAFLD Research Center, Department of Hepatology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China., Zheng TL; Artificial Intelligence Unit, Department of Medical Equipment, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China., Chi XL; Department of Hepatology, Guangdong Provincial Hospital of Chinese Medicine, the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China., Zhu YF; Department of Hepatology and Infection, Sir Run Run Shaw Hospital, Affiliated with School of Medicine, Zhejiang University, Hangzhou, China., Chen JJ; Hepatology Unit, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China.; Hepatology Unit, Zengcheng Branch, Nanfang Hospital, Southern Medical University, Guangzhou, China., Xu L; Department of Hepatology, Tianjin Second People's Hospital, Tianjin, China., Shi JP; Department of Liver Diseases, Hangzhou Normal University Affiliated Hospital, Hangzhou, China., Wang XD; Key Laboratory of Diagnosis and Treatment for the Development of Chronic Liver Disease in Zhejiang Province, Wenzhou, China., Zhao WG; Artificial Intelligence Unit, Department of Medical Equipment, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China., Byrne CD; Southampton National Institute for Health and Care Research Biomedical Research Centre, University Hospital Southampton & University of Southampton, Southampton General Hospital, Southampton, UK., Targher G; Section of Endocrinology, Diabetes and Metabolism, Department of Medicine, University of Verona, Verona, Italy., Rios RS; MAFLD Research Center, Department of Hepatology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China., Huang OY; MAFLD Research Center, Department of Hepatology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China., Tang LJ; MAFLD Research Center, Department of Hepatology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China., Zhang SJ; Artificial Intelligence Unit, Department of Medical Equipment, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China., Geng S; Artificial Intelligence Unit, Department of Medical Equipment, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China., Xiao HM; Department of Hepatology, Guangdong Provincial Hospital of Chinese Medicine, the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China., Chen SD; Department of Pathology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China., Zhang R; Department of Nutrition, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China., Zheng MH; MAFLD Research Center, Department of Hepatology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.; Key Laboratory of Diagnosis and Treatment for the Development of Chronic Liver Disease in Zhejiang Province, Wenzhou, China.; Institute of Hepatology, Wenzhou Medical University, Wenzhou, China.
Jazyk:	angličtina
Zdroj:	Hepatobiliary surgery and nutrition [Hepatobiliary Surg Nutr] 2023 Aug 01; Vol. 12 (4), pp. 507-522. Date of Electronic Publication: 2023 Mar 30.
DOI:	10.21037/hbsn-21-523
Abstrakt:	Background: There is an unmet need for accurate non-invasive methods to diagnose non-alcoholic steatohepatitis (NASH). Since impedance-based measurements of body composition are simple, repeatable and have a strong association with non-alcoholic fatty liver disease (NAFLD) severity, we aimed to develop a novel and fully automatic machine learning algorithm, consisting of a deep neural network based on impedance-based measurements of body composition to identify NASH [the bioeLectrical impEdance Analysis foR Nash (LEARN) algorithm]. Methods: A total of 1,259 consecutive subjects with suspected NAFLD were screened from six medical centers across China, of which 766 patients with biopsy-proven NAFLD were included in final analysis. These patients were randomly subdivided into the training and validation groups, in a ratio of 4:1. The LEARN algorithm was developed in the training group to identify NASH, and subsequently, tested in the validation group. Results: The LEARN algorithm utilizing impedance-based measurements of body composition along with age, sex, pre-existing hypertension and diabetes, was able to predict the likelihood of having NASH. This algorithm showed good discriminatory ability for identifying NASH in both the training and validation groups [area under the receiver operating characteristics (AUROC): 0.81, 95% CI: 0.77-0.84 and AUROC: 0.80, 95% CI: 0.73-0.87, respectively]. This algorithm also performed better than serum cytokeratin-18 neoepitope M30 (CK-18 M30) level or other non-invasive NASH scores (including HAIR, ION, NICE) for identifying NASH (P value <0.001). Additionally, the LEARN algorithm performed well in identifying NASH in different patient subgroups, as well as in subjects with partial missing body composition data. Conclusions: The LEARN algorithm, utilizing simple easily obtained measures, provides a fully automated, simple, non-invasive method for identifying NASH. Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://hbsn.amegroups.com/article/view/10.21037/hbsn-21-523/coif). MHZ serves as the unpaid editorial board member of Hepatobiliary Surgery and Nutrition. The other authors have no conflicts of interest to declare. (2023 Hepatobiliary Surgery and Nutrition. All rights reserved.)
Databáze:	MEDLINE
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