Objective:

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To investigate a new noninvasive diagnostic model for nonalcoholic fatty liver disease (NAFLD) based on features of tongue images.

Methods:

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Healthy controls and volunteers confirmed to have NAFLD by liver ultrasound were recruited from China-Japan Friendship Hospital between September 2018 and May 2019

then the anthropometric indexes and sampled tongue images were measured. The tongue images were labeled by features

based on a brief protocol

without knowing any other clinical data

after a series of corrections and data cleaning. The algorithm was trained on images using labels and several anthropometric indexes for inputs

utilizing machine learning technology. Finally

a logistic regression algorithm and a decision tree model were constructed as 2 diagnostic models for NAFLD.

Results:

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A total of 720 subjects were enrolled in this study

including 432 patients with NAFLD and 288 healthy volunteers. Of them

482 were randomly allocated into the training set and 238 into the validation set. The diagnostic model based on logistic regression exhibited excellent performance: in validation set

it achieved an accuracy of 86.98%

sensitivity of 91.43%

and specificity of 80.61%; with an area under the curve (AUC) of 0.93 [95% confidence interval (CI) 0.68–0.98]. The decision tree model achieved an accuracy of 81.09%

sensitivity of 91.43%

and specificity of 66.33%; with an AUC of 0.89 (95% CI 0.66–0.92) in validation set.

Conclusions:

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The features of tongue images were associated with NAFLD. Both the 2 diagnostic models

which would be convenient

noninvasive

lightweight

rapid

and inexpensive technical references for early screening

can accurately distinguish NAFLD and are worth further study.