Li, Gang, Zheng, Tian-Lei, Chi, Xiao-Ling, Zhu, Yong-Fen, Chen, Jin-Jun, Xu, Liang, Shi, Jun-Ping, Wang, Xiao-Dong, Zhao, Wei-Guo, Byrne, Christopher, Targher, Giovanni, Rios, Rafael S., Huang, Ou-Yang, Tang, Liang-Jie, Zhang, Shi-Jin, Geng, Shi, Xiao, Huan-Ming, Chen, Sui-Dan, Zhang, Rui and Zheng, Ming-Hua (2023) LEARN algorithm: a novel option for predicting non-alcoholic steatohepatitis. Hepatobiliary Surgery and Nutrition, 12 (4), 507-522. (doi:10.21037/hbsn-21-523).
Abstract
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.
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