A multi-step approach for tongue image classification in patients with diabetes

Li, Jun; Jiang, Tao; Tu, Liping; Cui, Longtao; Cui, Ji; Ma, Xuxiang; Yao, Xinghua; Shi, Yulin; Wang, Sihan; Wang, Yu; Liu, Jiayi; Li, Yongzhi; Zhou, Changle; Hu, Xiaojuan; Xu, Jiatuo

doi:10.1016/j.compbiomed.2022.105935

Cited by 23 publications

(17 citation statements)

References 41 publications

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“…Li et al [ 170 ] discovered a novel concept for Daibetics patients using Vision Transformer and Tongue Diagnosis Analysis System (TDAS). Diabetes is a long-term illness that is prevalent and highly prevalent Globally, especially in China.…”

Section: Deep Learning For Medical Image Analysis and Cadmentioning

confidence: 99%

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Iqbal

Qureshi

et al. 2023

Arch Computat Methods Eng

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Convolutional neural network (CNN) has shown dissuasive accomplishment on different areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information Retrieval, Medical Image Registration, Multi-lingual translation, Local language Processing, Anomaly Detection on video and Speech Recognition. CNN is a special type of Neural Network, which has compelling and effective learning ability to learn features at several steps during augmentation of the data. Recently, different interesting and inspiring ideas of Deep Learning (DL) such as different activation functions, hyperparameter optimization, regularization, momentum and loss functions has improved the performance, operation and execution of CNN Different internal architecture innovation of CNN and different representational style of CNN has significantly improved the performance. This survey focuses on internal taxonomy of deep learning, different models of vonvolutional neural network, especially depth and width of models and in addition CNN components, applications and current challenges of deep learning.

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Section: Deep Learning For Medical Image Analysis and Cadmentioning

confidence: 99%

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Iqbal

Qureshi

et al. 2023

Arch Computat Methods Eng

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“…As non-invasive indicators, tongue image characteristics have been used for the surveillance of a broad spectrum of diseases, inspired by the diagnosis experience in traditional Chinese medicine (TCM) [ 16 , 17 , 18 , 19 , 20 ]. Tongue image characteristics, including shape, color, and tongue coating, are believed to reflect the health condition, or the severity and progress of disease, especially for digestive diseases as the tongue is anatomically connected to the digestive system organs.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, it was revealed that tongue surface and color characteristics could be used as indicators to assist in gastric cancer diagnosis [ 23 , 24 ]. Moreover, morphological markers based on tongue images are considered to be valuable for risk screening for other diseases, such as diabetes, fatty liver disease, and COVID-19 [ 17 , 25 , 26 , 27 ]. From the pathologic and etiologic perspectives, the distribution of microorganisms on the tongue coating has also been found to be related to gastric diseases, which helps uncover non-invasive microbial markers for gastric disease risk screening [ 28 , 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

Construction of Tongue Image-Based Machine Learning Model for Screening Patients with Gastric Precancerous Lesions

Zhang

et al. 2023

JPM

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Screening patients with precancerous lesions of gastric cancer (PLGC) is important for gastric cancer prevention. The accuracy and convenience of PLGC screening could be improved with the use of machine learning methodologies to uncover and integrate valuable characteristics of noninvasive medical images related to PLGC. In this study, we therefore focused on tongue images and for the first time constructed a tongue image-based PLGC screening deep learning model (AITongue). The AITongue model uncovered potential associations between tongue image characteristics and PLGC, and integrated canonical risk factors, including age, sex, and Hp infection. Five-fold cross validation analysis on an independent cohort of 1995 patients revealed the AITongue model could screen PLGC individuals with an AUC of 0.75, 10.3% higher than that of the model with only including canonical risk factors. Of note, we investigated the value of the AITongue model in predicting PLGC risk by establishing a prospective PLGC follow-up cohort, reaching an AUC of 0.71. In addition, we developed a smartphone-based app screening system to enhance the application convenience of the AITongue model in the natural population from high-risk areas of gastric cancer in China. Collectively, our study has demonstrated the value of tongue image characteristics in PLGC screening and risk prediction.

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“…Thus, non-invasive and highly accurate screening models for PLGC patients are urgently needed. As non-invasive indicators, tongue image characteristics were used for the surveillance of a broad spectrum of diseases, inspired by the diagnosis experience in traditional Chinese medicine [15][16][17][18][19][20][21]. Tongue image characteristics, including shape, color, and tongue coating, are believed to reflect the state of health and the progress and severity of disease, especially for digestive diseases as the tongue is physically connected to the digestive system.…”

Section: Introductionmentioning

confidence: 99%

“…From the pathologic perspective, the distribution of microorganisms on tongue coating has also been suggested to be related to the distribution in gastric tissue, which can be used as a marker for gastric disease risk screening [25][26][27]. Moreover, morphological markers based on tongue images are considered to be valuable for risk screening for other diseases, such as diabetes, fatty liver disease, and CoVID-19 [16,[28][29][30][31]. The above studies indicated that tongue image characteristics can assist in disease screening.…”

Section: Introductionmentioning

confidence: 99%

Construction of tongue image-based machine learning model for screening patients with gastric precancerous lesions

Zhang

2023

Preprint

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Screening patients with precancerous lesions of gastric cancer (PLGC) is important for gastric cancer prevention. It could improve the accuracy and convenience of PLGC screening to uncover and integrate valuable characteristics of noninvasive medical images involving in PLGC, by applying machine learning methodologies. In this study, based on unbiasedly uncovering potential associations between tongue image characteristics and PLGC and integrating gastric cancer-related canonical risk factors, including age, sex, Hp infection, we focused on tongue images and constructed a tongue image-based PLGC screening deep learning model (AITongue). Then, validation analysis on an independent cohort of 1,995 patients revealed the AITongue model could screen PLGC individuals with an AUC of 0.75, 10.3% higher than that of the model constructed with gastric cancer-related canonical risk factors. Of note, we investigated the value of the AITongue model in predicting PLGC risk by establishing a prospective PLGC follow-up cohort, reaching an AUC of 0.71. In addition, we have developed a smartphone-based App screening system to enhance the application convenience of the AITongue model in the natural population. Collectively, our study has demonstrated the value of tongue image characteristics in PLGC screening and risk prediction.

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A multi-step approach for tongue image classification in patients with diabetes

Cited by 23 publications

References 41 publications

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Construction of Tongue Image-Based Machine Learning Model for Screening Patients with Gastric Precancerous Lesions

Construction of tongue image-based machine learning model for screening patients with gastric precancerous lesions

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