Constructing tongue coating recognition model using deep transfer learning to assist syndrome diagnosis and its potential in noninvasive ethnopharmacological evaluation
Constructing tongue coating recognition model using deep transfer learning to assist syndrome diagnosis and its potential in noninvasive ethnopharmacological evaluation
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DOI：
10.1016/j.jep.2021.114905
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发表时间：
2022-03-01
影响因子：
5.4
通讯作者：
Chen, Jianxin
中科院分区：
文献类型：
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作者：
Wang, Xu;Wang, Xinrong;Chen, Jianxin
民族药理学相关性：舌苔已被用作传统中医（TCM）健康的有效标志。在中医理论中，油腻程度与湿邪强弱密切相关。先前的实证研究和我们的系统综述已经显示了油腻涂层与各种疾病之间的关系，包括胃肠病，冠心病和2019冠状病毒病（COVID-19）。然而，目前还缺乏客观、智能的油膜及其相关疾病的识别方法。人工智能舌象识别模型的构建将为中医证候诊断和疗效评价提供重要的方法，并有助于从中医理论的角度理解民族药理学机制。研究目的：本研究旨在建立一种用于腻苔识别的人工智能模型，并探索其在COVID-19中的应用。材料与方法：本文采用卷积神经网络技术和来自标准设备的相对较大（N = 1486）的舌图像集，开发了腻苔识别网络（GreasyCoatNet）。使用交叉验证程序和由普通相机捕获的新数据集（N = 50）进行测试。此外，还比较了GreasyCoatNet和Doctor的准确率和时间效率。最后，该模型被转移到识别COVID-19的油腻涂层水平。结果如下：在交叉验证的情况下，3级油性涂层分类的总体准确率为88.8%，在新数据集上的准确率为82.0%，表明GreasyCoatNet可以从不同的数据集获得稳健的油性涂层估计。此外，我们进行了用户研究，以确认我们的GreasyCoatNet优于中医师，但仅消耗医生约1%的检查时间。重要的是，我们证明了GreasyCoatNet，沿着迁移学习，与直接在患者与对照数据集上训练分类器相比，可以构建更合适的COVID-19分类器。因此，我们通过对通用的GreasyCoatNet进行微调，得到了一个疾病特异性的深度学习网络。结论：该框架为舌象鉴别、中医证候诊断、疾病进展追踪和干预疗效评价提供了重要的研究范式，具有独特的临床应用潜力。
Ethnopharmacological relevance: Tongue coating has been used as an effective signature of health in traditional Chinese medicine (TCM). The level of greasy coating closely relates to the strength of dampness or pathogenic qi in TCM theory. Previous empirical studies and our systematic review have shown the relation between greasy coating and various diseases, including gastroenteropathy, coronary heart disease, and coronavirus disease 2019 (COVID-19). However, the objective and intelligent greasy coating and related diseases recognition methods are still lacking. The construction of the artificial intelligent tongue recognition models may provide important syndrome diagnosis and efficacy evaluation methods, and contribute to the understanding of ethno-pharmacological mechanisms based on TCM theory. Aim of the study: The present study aimed to develop an artificial intelligent model for greasy tongue coating recognition and explore its application in COVID-19.Materials and methods: Herein, we developed greasy tongue coating recognition networks (GreasyCoatNet) using convolutional neural network technique and a relatively large (N = 1486) set of tongue images from standard devices. Tests were performed using both cross-validation procedures and a new dataset (N = 50) captured by common cameras. Besides, the accuracy and time efficiency comparisons between the GreasyCoatNet and doc-tors were also conducted. Finally, the model was transferred to recognize the greasy coating level of COVID-19. Results: The overall accuracy in 3-level greasy coating classification with cross-validation was 88.8% and ac-curacy on new dataset was 82.0%, indicating that GreasyCoatNet can obtain robust greasy coating estimates from diverse datasets. In addition, we conducted user study to confirm that our GreasyCoatNet outperforms TCM practitioners, yet only consuming roughly 1% of doctors' examination time. Critically, we demonstrated that GreasyCoatNet, along with transfer learning, can construct more proper classifier of COVID-19, compared to directly training classifier on patient versus control datasets. We, therefore, derived a disease-specific deep learning network by finetuning the generic GreasyCoatNet. Conclusions: Our framework may provide an important research paradigm for differentiating tongue character-istics, diagnosing TCM syndrome, tracking disease progression, and evaluating intervention efficacy, exhibiting its unique potential in clinical applications.