Deep Learning Multi-label Tongue Image Analysis and Its Application in a Population Undergoing Routine Medical Checkup

Jiang, Tao; Lu, Zhou; Hu, Xiaojuan; Zeng, Ling-Zhi; Ma, Xuxiang; Cui, Ji; Tu, Liping; Zhou, Changle; Yao, Xinghua; Xu, Jiatuo

doi:10.1155/2022/3384209

Cited by 12 publications

(4 citation statements)

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“…The success of our CNN age prediction models confirms the existing anatomical and physiological evidence that with increased age, our tongue indeed undergoes systematic aging [35,[59][60][61][62][63][64]. To the best of our knowledge, we are the first to demonstrate that deep CNN models are able to make age predictions based on the tongue image alone.…”

Section: Discussionsupporting

confidence: 78%

“…Our models' gender classification is highly accurate, with the mean accuracy and standard deviation being 79.5% ± 0.8%. The high accuracy of gender classification may be due to the fact that male and female tongues are distinct in terms of tongue morphology and hemoglobin concentrations [35,64,65]. The distinct myoglobin and hemoglobin levels between males and females [66,67] might result in significant color differences between male tongues and female tongues.…”

Section: Discussionmentioning

confidence: 99%

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Using advanced convolutional neural network approaches to reveal patient age, gender, and weight based on tongue images

Wei

et al. 2023

Preprint

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The human tongue has been long believed to be a window to provide important insights into a patient's health in medicine. The present study introduced a novel approach to predict patient age, gender, and weight inferences based on tongue images using pre-trained deep CNNs. Our results demonstrated that the deep CNN models trained on dorsal tongue images produced excellent results for age prediction with a Pearson correlation coefficient of 0.71 and a mean absolute error of 8.5 years. We also obtained an excellent classification of gender, with a mean accuracy of 80% and an AUC of 88%. The model also obtained a moderate level of accuracy for weight prediction, with a Pearson correlation coefficient of 0.39 and a mean absolute error of 9.06 kilograms. These findings support our hypothesis that the human tongue contains crucial information about a patient. This study demonstrated the feasibility of using the pre-trained deep CNNs along with a large tongue image dataset to develop computational models to predict patient medical conditions for non-invasive, convenient, and inexpensive patient health monitoring and diagnosis.

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Section: Discussionsupporting

confidence: 78%

Section: Discussionmentioning

confidence: 99%

Using advanced convolutional neural network approaches to reveal patient age, gender, and weight based on tongue images

Wei

et al. 2023

Preprint

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“…Second, there are many tongue image feature types, as shown in Figure 1 , and they are more likely to be comprehensively judged as the probability of a certain type of health state. 10 , 121 The health state abstracted from the whole body system is related to the concept of “syndrome.” TCM theory believes that the human body is an interrelated system, the running state and abnormal symptoms of different body parts are interrelated and mutually affected, 1 , 122 showing a certain distribution law, and syndrome types could be treated as the clusters summarized by this distribution. Therefore, not only do the features of the tongue image need to be integrated but also the features from other diagnostic methods, to better distinguish syndromes.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

A survey of artificial intelligence in tongue image for disease diagnosis and syndrome differentiation

Liu

Peng

et al. 2023

DIGITAL HEALTH

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The rapid development of artificial intelligence technology has gradually extended from the general field to all walks of life, and intelligent tongue diagnosis is the product of a miraculous connection between this new discipline and traditional disciplines. We reviewed the deep learning methods and machine learning applied in tongue image analysis that have been studied in the last 5 years, focusing on tongue image calibration, detection, segmentation, and classification of diseases, syndromes, and symptoms/signs. Introducing technical evolutions or emerging technologies were applied in tongue image analysis; as we have noticed, attention mechanism, multiscale features, and prior knowledge were successfully applied in it, and we emphasized the value of combining deep learning with traditional methods. We also pointed out two major problems concerned with data set construction and the low reliability of performance evaluation that exist in this field based on the basic essence of tongue diagnosis in traditional Chinese medicine. Finally, a perspective on the future of intelligent tongue diagnosis was presented; we believe that the self-supervised method, multimodal information fusion, and the study of tongue pathology will have great research significance.

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“…The faster R-CNN, a region-based network, achieved an accuracy of 90.67%. [39] considered 11 features on the tongue surface.…”

Section: Imagesmentioning

confidence: 99%

Convolution Neural Network Based Multi-Label Disease Detection Using Smartphone Captured Tongue Images

Bhatnagar,

Bansod

2024

Applied Sciences

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Purpose: Tongue image analysis for disease diagnosis is an ancient, traditional, non-invasive diagnostic technique widely used by traditional medicine practitioners. Deep learning-based multi-label disease detection models have tremendous potential for clinical decision support systems because they facilitate preliminary diagnosis. Methods: In this work, we propose a multi-label disease detection pipeline where observation and analysis of tongue images captured and received via smartphones assist in predicting the health status of an individual. Subjects, who consult collaborating physicians, voluntarily provide all images. Images thus acquired are first and foremost classified either into a diseased or a normal category by a 5-fold cross-validation algorithm using a convolutional neural network (MobileNetV2) model for binary classification. Once it predicts the diseased label, the disease prediction algorithm based on DenseNet-121 uses the image to diagnose single or multiple disease labels. Results: The MobileNetV2 architecture-based disease detection model achieved an average accuracy of 93% in distinguishing between diseased and normal, healthy tongues, whereas the multilabel disease classification model produced more than 90% accurate results for the disease class labels considered, strongly indicating a successful outcome with the smartphone-captured image dataset. Conclusion: AI-based image analysis shows promising results, and an extensive dataset could provide further improvements to this approach. Experimenting with smartphone images opens a great opportunity to provide preliminary health status to individuals at remote locations as well, prior to further treatment and diagnosis, using the concept of telemedicine.

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Deep Learning Multi-label Tongue Image Analysis and Its Application in a Population Undergoing Routine Medical Checkup

Cited by 12 publications

References 50 publications

Using advanced convolutional neural network approaches to reveal patient age, gender, and weight based on tongue images

Using advanced convolutional neural network approaches to reveal patient age, gender, and weight based on tongue images

A survey of artificial intelligence in tongue image for disease diagnosis and syndrome differentiation

Convolution Neural Network Based Multi-Label Disease Detection Using Smartphone Captured Tongue Images

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