Exploring hepatic fibrosis screening via deep learning analysis of tongue images

IF 4.3 3区材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC ACS Applied Electronic Materials Pub Date : 2024-03-06 DOI:10.1016/j.jtcme.2024.03.010

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Abstract

Background

Tongue inspection, an essential diagnostic method in Traditional Chinese Medicine (TCM), has the potential for early-stage disease screening. This study aimed to evaluate the effectiveness of deep learning-based analysis of tongue images for hepatic fibrosis screening.

Methods

A total of 1083 tongue images were collected from 741 patients and divided into training, validation, and test sets. DenseNet-201, a convolutional neural network, was employed to train the AI model using these tongue images. The predictive performance of AI was assessed and compared with that of FIB-4, using real-time two-dimensional shear wave elastography as the reference standard.

Results

The proposed AI model achieved an accuracy of 0.845 (95% CI: 0.79–0.90) and 0.814 (95% CI: 0.76–0.87) in the validation and test sets, respectively, with negative predictive values (NPVs) exceeding 90% in both sets. The AI model outperformed FIB-4 in all aspects, and when combined with FIB-4, the NPV reached 94.4%.

Conclusion

Tongue inspection, with the assistance of AI, could serve as a first-line screening method for hepatic fibrosis.

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通过舌头图像的深度学习分析探索肝纤维化筛查

背景舌象检查是中医（TCM）的一种重要诊断方法，具有早期疾病筛查的潜力。本研究旨在评估基于深度学习的舌象分析在肝纤维化筛查中的有效性。方法从 741 名患者身上共收集了 1083 张舌象，并将其分为训练集、验证集和测试集。采用卷积神经网络 DenseNet-201 利用这些舌头图像训练人工智能模型。结果所提出的人工智能模型在验证集和测试集中的准确率分别达到了 0.845（95% CI：0.79-0.90）和 0.814（95% CI：0.76-0.87），负预测值（NPV）均超过了 90%。人工智能模型在所有方面都优于 FIB-4，当与 FIB-4 结合使用时，NPV 达到 94.4%。

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