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Objective <p>To systematically evaluate and quantify the diagnostic accuracy and performance of machine ML techniques for the detection of NAFLD, and to compare the performance of ML when assisting different diagnostic modalities, in order to provide the promising strategy for non-invasive and accurate diagnosis of NAFLD.</p> Methods <p>A literature search was performed in the PubMed, Web of Science, Cochrane Library, and EMBASE databases from the establishment of the databases to March 2025. The quality of studies and risk of bias were assessed via the QUADAS-2 and the PROBAST. Studies reporting AUC, sensitivity, and specificity were included. Data analysis and plotting were conducted using R (version 4.3.1), Stata (version 17.0), and Origin 2022. This study adhered to the PRISMA guidelines.</p> Results <p>A systematic review and bias assessment were conducted on 24 articles, and a meta-analysis was performed on 21 sets of data from 18 articles. The sensitivity, specificity, and AUC values for the internal validation dataset were 0.82 [95% CI 0.77—0.86], 0.81 [95% CI 0.77—0.85], and 0.88 [95% CI 0.85—0.91], respectively. For the external validation dataset, the corresponding values were 0.80 [95% CI 0.70—0.87], 0.72 [95% CI 0.56—0.84], and 0.83 [95% CI 0.80—0.86]. Heterogeneity was observed among the studies, and subgroup analysis indicated that study design, reference standard, outcome, cross-validation and model type may be potential sources of heterogeneity. ML models assisting imaging achieved higher diagnostic accuracy than those assisting clinical variables/routine serological markers.</p> Conclusion <p>ML-assisted imaging achieved promising diagnostic accuracy for NAFLD detection, offering the potential to assist clinicians in making accurate diagnoses and improving clinical decision-making. However, the lack of external validation limited the applicability and generalizability of the model. It is necessary to conduct multi-center studies and external validation in the future.</p>

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Applications of machine learning in the diagnosis of non-alcoholic fatty liver disease: a systematic review and meta-analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月3日

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  • Luying Qi,
  • Baiwang Li,
  • Jingnan Xue,
  • Xiangpeng Wu,
  • Hupo Bian,
  • Jieqiong Chen,
  • Hongxing Zhao

摘要

Objective

To systematically evaluate and quantify the diagnostic accuracy and performance of machine ML techniques for the detection of NAFLD, and to compare the performance of ML when assisting different diagnostic modalities, in order to provide the promising strategy for non-invasive and accurate diagnosis of NAFLD.

Methods

A literature search was performed in the PubMed, Web of Science, Cochrane Library, and EMBASE databases from the establishment of the databases to March 2025. The quality of studies and risk of bias were assessed via the QUADAS-2 and the PROBAST. Studies reporting AUC, sensitivity, and specificity were included. Data analysis and plotting were conducted using R (version 4.3.1), Stata (version 17.0), and Origin 2022. This study adhered to the PRISMA guidelines.

Results

A systematic review and bias assessment were conducted on 24 articles, and a meta-analysis was performed on 21 sets of data from 18 articles. The sensitivity, specificity, and AUC values for the internal validation dataset were 0.82 [95% CI 0.77—0.86], 0.81 [95% CI 0.77—0.85], and 0.88 [95% CI 0.85—0.91], respectively. For the external validation dataset, the corresponding values were 0.80 [95% CI 0.70—0.87], 0.72 [95% CI 0.56—0.84], and 0.83 [95% CI 0.80—0.86]. Heterogeneity was observed among the studies, and subgroup analysis indicated that study design, reference standard, outcome, cross-validation and model type may be potential sources of heterogeneity. ML models assisting imaging achieved higher diagnostic accuracy than those assisting clinical variables/routine serological markers.

Conclusion

ML-assisted imaging achieved promising diagnostic accuracy for NAFLD detection, offering the potential to assist clinicians in making accurate diagnoses and improving clinical decision-making. However, the lack of external validation limited the applicability and generalizability of the model. It is necessary to conduct multi-center studies and external validation in the future.