A Framework to Predict Gastric Cancer Based on Tongue Features and Deep Learning

Zhu, Xiaolong; Ma, Yuhang; Jiu-zhang, Men; Chen, Xue; Cao, Xiyuan; Zhang, Zhidong

doi:10.3390/mi14010053

Cited by 11 publications

(7 citation statements)

References 47 publications

(44 reference statements)

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“…According to the principles of TCM, tongue manifestation serves as a crucial indication of "collaterals" [30]. It plays a significant role in various disorders such as gastric diseases [21], rheumatic conditions [31], hepatic disorders [32], and tumors [10], among others. Tongue color, thickness, moisture, and other changes, as well as the rich network of vessels in the tongue area [30], to a certain extent, can reflect the pathological changes in the spleen, stomach, internal organs, joints, and many other parts.…”

Section: Discussionmentioning

confidence: 99%

“…Early studies have indicated that non-invasive features, including imaging characteristics and clinical phenotypic information, have the potential to predict the occurrence and progression of PLGC. The theory of Traditional Chinese medicine (TCM) suggests that the tongue’s shape, color, size, and coating characteristics detected using tongue images reflect health status and disease severity/progression, and recent studies have shown the potential for tongue surface and color characteristics to assist in the diagnosis of PLGC [9, 10]. For digestive diseases, tongue images characteristics have been found to correlate with gastroscopic observations and predict gastric mucosal health [11, 12].…”

Section: Introductionmentioning

confidence: 99%

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Pre-endoscopic screening of precancerous lesions in gastric cancer using deep learning

Wang,

Zhang,

Zhang

et al. 2024

Preprint

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Objective: Given the high cost of endoscopy in gastric cancer (GC) screening, there is an urgent need to explore cost-effective methods for the large-scale prediction of precancerous lesions of gastric cancer (PLGC). We aim to construct a hierarchical artificial intelligence-based multimodal non-invasive method for pre-endoscopic risk screening, to provide tailored recommendations for endoscopy. Design: From December 2022 to December 2023, a large-scale screening study was conducted in Fujian, China. Based on traditional Chinese medicine theory, we simultaneously collected tongue images and inquiry information from 1034 participants, considering the potential of these data for PLGC screening. Then, we introduced inquiry information for the first time, forming a multimodality artificial intelligence model to integrate tongue images and inquiry information for pre-endoscopic screening. Moreover, we validated this approach in another independent external validation cohort, comprising 143 participants from the China-Japan Friendship Hospital. Results: A multimodality artificial intelligence-assisted pre-endoscopic screening model based on tongue images and inquiry information (AITonguequiry) was constructed, adopting a hierarchical prediction strategy, achieving tailored endoscopic recommendations. Validation analysis revealed that the area under the curve (AUC) values of AITonguequiry were 0.74 for PLGC (95% confidence interval (CI) 0.71 to 0.76, p < 0.05) and 0.82 for high-risk PLGC (95% CI 0.82 to 0.83, p < 0.05), which were significantly and robustly better than those of the independent use of either tongue images or inquiry information alone, and also demonstrated superior performance compared to existing screening methodologies. In the independent external verification, the AUC values were 0.69 for PLGC and 0.76 for high-risk PLGC. Conclusion: Our AITonguequiry artificial intelligence model, for the first time, incorporates inquiry information and tongue images, leading to a higher precision and finer-grained pre-endoscopic screening of PLGC. This enhances patient screening efficiency and alleviates patient burden.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pre-endoscopic screening of precancerous lesions in gastric cancer using deep learning

Wang,

Zhang,

Zhang

et al. 2024

Preprint

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“…Wu et al 5 studied the tongue diagnosis indices for gastroesophageal reflux disease (GERD), and they used an automatic tongue diagnosis system (ATDS) to extract the tongue indices and found that the saliva amount ( p = .009) and thickness of the tongue's fur ( p = .036), especially that in the spleen–stomach area (%) ( p = .029), were significantly greater in patients with GERD. Handcraft features of tongue images from physicians were weighted and filtered by the XGBT algorithm in research, 86 and the EfficientNet network was used to classify gastric cancer based on the selected features. Meng et al 87 used tongue image high-level features extracted from a CNN framework to diagnose gastritis and found that the deep learning framework extracted more suitable features than histogram of oriented gradients (HOG), local binary pattern (LBP), and scale-invariant feature transform (SIFT), which extract the handcrafted low-level features.…”

Section: Tongue Image For Disease Diagnosismentioning

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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“…For example, recent studies have indicated that tongue image characteristics show correlations with gastroscopic observations and could be used to predict gastric mucosal health [ 21 , 22 ]. 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 ].…”

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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A Framework to Predict Gastric Cancer Based on Tongue Features and Deep Learning

Cited by 11 publications

References 47 publications

Pre-endoscopic screening of precancerous lesions in gastric cancer using deep learning

Pre-endoscopic screening of precancerous lesions in gastric cancer using deep learning

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

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

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