An Intelligent Tongue Diagnosis System via Deep Learning on the Android Platform

Yang, Zibin; Zhao, Yuping; Yu, Jiarui; Mao, Xiaobo; Xu, Huaxing; Huang, Luqi

doi:10.3390/diagnostics12102451

Cited by 8 publications

(3 citation statements)

References 59 publications

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“…One team ( 12 ) developed an intelligent tongue recognition system leveraging deep learning to rapidly and accurately identify pathological characteristics in tongue and facial diagnostics. The tongue diagnosis framework ( 13 ) incorporates YOLOv5s6, U-Net, and MobileNetV3 networks for tongue segmentation, boundary of regions, and feature classification of teeth marks, spots, and fissures. The system achieved classification accuracies of 93.33, 89.60, and 97.67%, respectively, representing a valuable reference for objective, intelligent tongue examination.…”

Section: Introductionmentioning

confidence: 99%

Opportunities and challenges of traditional Chinese medicine doctors in the era of artificial intelligence

Li,

Ge,

Liu

et al. 2024

Front. Med.

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With the exponential advancement of artificial intelligence (AI) technology, the realm of medicine is experiencing a paradigm shift, engendering a multitude of prospects and trials for healthcare practitioners, encompassing those devoted to the practice of traditional Chinese medicine (TCM). This study explores the evolving landscape for TCM practitioners in the AI era, emphasizing that while AI can be helpful, it cannot replace the role of TCM practitioners. It is paramount to underscore the intrinsic worth of human expertise, accentuating that artificial intelligence (AI) is merely an instrument. On the one hand, AI-enabled tools like intelligent symptom checkers, diagnostic assistance systems, and personalized treatment plans can augment TCM practitioners’ expertise and capacity, improving diagnosis accuracy and treatment efficacy. AI-empowered collaborations between Western medicine and TCM can strengthen holistic care. On the other hand, AI may disrupt conventional TCM workflow and doctor-patient relationships. Maintaining the humanistic spirit of TCM while embracing AI requires upholding professional ethics and establishing appropriate regulations. To leverage AI while retaining the essence of TCM, practitioners need to hone holistic analytical skills and see AI as complementary. By highlighting promising applications and potential risks of AI in TCM, this study provides strategic insights for stakeholders to promote the integrated development of AI and TCM for better patient outcomes. With proper implementation, AI can become a valuable assistant for TCM practitioners to elevate healthcare quality.

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Section: Introductionmentioning

confidence: 99%

Opportunities and challenges of traditional Chinese medicine doctors in the era of artificial intelligence

Li,

Ge,

Liu

et al. 2024

Front. Med.

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show abstract

“…For example, Iqbal et al applied transfer learning to detect the synovial fluid of human knee joint [9]. Several studies also applied Gradientweighted Class Activation Mapping or other visualization techniques to roughly locate tongue features [10][11][12]. Only two applied deep learning object detection techniques to mark tongue features.…”

Section: Introductionmentioning

confidence: 99%

Tongue feature dataset construction and real-time detection

Chang,

Chen,

et al. 2024

PLoS ONE

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Background Tongue diagnosis in traditional Chinese medicine (TCM) provides clinically important, objective evidence from direct observation of specific features that assist with diagnosis. However, the current interpretation of tongue features requires a significant amount of manpower and time. TCM physicians may have different interpretations of features displayed by the same tongue. An automated interpretation system that interprets tongue features would expedite the interpretation process and yield more consistent results. Materials and methods This study applied deep learning visualization to tongue diagnosis. After collecting tongue images and corresponding interpretation reports by TCM physicians in a single teaching hospital, various tongue features such as fissures, tooth marks, and different types of coatings were annotated manually with rectangles. These annotated data and images were used to train a deep learning object detection model. Upon completion of training, the position of each tongue feature was dynamically marked. Results A large high-quality manually annotated tongue feature dataset was constructed and analyzed. A detection model was trained with average precision (AP) 47.67%, 58.94%, 71.25% and 59.78% for fissures, tooth marks, thick and yellow coatings, respectively. At over 40 frames per second on a NVIDIA GeForce GTX 1060, the model was capable of detecting tongue features from any viewpoint in real time. Conclusions/Significance This study constructed a tongue feature dataset and trained a deep learning object detection model to locate tongue features in real time. The model provided interpretability and intuitiveness that are often lacking in general neural network models and implies good feasibility for clinical application.

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“…Tongue diagnosis using smartphones can avoid the above problems and has enormous advantages. Smartphones are massively widespread in society and many studies have also focused on the great value of smartphones and personal smart devices for health monitoring [28][29][30]. The smartphone was more convenient and cheaper to take tongue images than TIAI, so it was suitable for public health monitoring and was of great significance to establishing health records and disease prevention.…”

Section: Introductionmentioning

confidence: 99%

Development of attention-based robust deep learning model for tongue diagnosis by smartphone

Gao

Hou

et al. 2023

Preprint

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Compared with tongue diagnosis using tongue image analyzers, tongue diagnosis by smartphone has great advantages in convenience and cost for universal health monitoring, but its accuracy is affected by the shooting conditions of smartphones. Developing deep learning models with high accuracy and robustness to changes in the shooting environment for tongue diagnosis by smartphone and determining the influence of environmental changes on accuracy are necessary. In our study, a dataset of 9003 images was constructed after image pre-processing and labeling. Next, we developed an attention-based deep learning model (Deep Tongue) for 8 subtasks of tongue diagnosis, including the spotted tongue, teeth-marked tongue, and fissure tongue et al, which the average AUC of was 0.90, higher than the baseline model (ResNet50) by 0.10. Finally, we analyzed the objective reasons, the brightness of the environment and the hue of images, affecting the accuracy of tongue diagnosis by smartphone through a consistency experiment of direct subject inspection and tongue image inspection. Finally, we determined the influence of environmental changes on accuracy to quantify the robustness of the Deep Tongue model through simulation experiments. Overall, the Deep Tongue model achieved a higher and more stable classification accuracy of seven tongue diagnosis tasks in the complex shooting environment of the smartphone, and the classification of tongue coating (yellow/white) was found to be sensitive to the hue of the images and therefore unreliable without stricter shooting conditions and color correction.

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An Intelligent Tongue Diagnosis System via Deep Learning on the Android Platform

Cited by 8 publications

References 59 publications

Opportunities and challenges of traditional Chinese medicine doctors in the era of artificial intelligence

Opportunities and challenges of traditional Chinese medicine doctors in the era of artificial intelligence

Tongue feature dataset construction and real-time detection

Development of attention-based robust deep learning model for tongue diagnosis by smartphone

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