{"title":"通过舌头图像的深度学习分析探索肝纤维化筛查","authors":"","doi":"10.1016/j.jtcme.2024.03.010","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>Tongue inspection, an essential diagnostic method in Traditional Chinese Medicine (TCM), has the potential for early-stage disease screening. This study aimed to evaluate the effectiveness of deep learning-based analysis of tongue images for hepatic fibrosis screening.</p></div><div><h3>Methods</h3><p>A total of 1083 tongue images were collected from 741 patients and divided into training, validation, and test sets. DenseNet-201, a convolutional neural network, was employed to train the AI model using these tongue images. The predictive performance of AI was assessed and compared with that of FIB-4, using real-time two-dimensional shear wave elastography as the reference standard.</p></div><div><h3>Results</h3><p>The proposed AI model achieved an accuracy of 0.845 (95% CI: 0.79–0.90) and 0.814 (95% CI: 0.76–0.87) in the validation and test sets, respectively, with negative predictive values (NPVs) exceeding 90% in both sets. The AI model outperformed FIB-4 in all aspects, and when combined with FIB-4, the NPV reached 94.4%.</p></div><div><h3>Conclusion</h3><p>Tongue inspection, with the assistance of AI, could serve as a first-line screening method for hepatic fibrosis.</p></div>","PeriodicalId":3,"journal":{"name":"ACS Applied Electronic Materials","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2225411024000294/pdfft?md5=95254b587bfa96b8670590c12fc58de5&pid=1-s2.0-S2225411024000294-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Exploring hepatic fibrosis screening via deep learning analysis of tongue images\",\"authors\":\"\",\"doi\":\"10.1016/j.jtcme.2024.03.010\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><p>Tongue inspection, an essential diagnostic method in Traditional Chinese Medicine (TCM), has the potential for early-stage disease screening. This study aimed to evaluate the effectiveness of deep learning-based analysis of tongue images for hepatic fibrosis screening.</p></div><div><h3>Methods</h3><p>A total of 1083 tongue images were collected from 741 patients and divided into training, validation, and test sets. DenseNet-201, a convolutional neural network, was employed to train the AI model using these tongue images. The predictive performance of AI was assessed and compared with that of FIB-4, using real-time two-dimensional shear wave elastography as the reference standard.</p></div><div><h3>Results</h3><p>The proposed AI model achieved an accuracy of 0.845 (95% CI: 0.79–0.90) and 0.814 (95% CI: 0.76–0.87) in the validation and test sets, respectively, with negative predictive values (NPVs) exceeding 90% in both sets. The AI model outperformed FIB-4 in all aspects, and when combined with FIB-4, the NPV reached 94.4%.</p></div><div><h3>Conclusion</h3><p>Tongue inspection, with the assistance of AI, could serve as a first-line screening method for hepatic fibrosis.</p></div>\",\"PeriodicalId\":3,\"journal\":{\"name\":\"ACS Applied Electronic Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-03-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2225411024000294/pdfft?md5=95254b587bfa96b8670590c12fc58de5&pid=1-s2.0-S2225411024000294-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Electronic Materials\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2225411024000294\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Electronic Materials","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2225411024000294","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Exploring hepatic fibrosis screening via deep learning analysis of tongue images
Background
Tongue inspection, an essential diagnostic method in Traditional Chinese Medicine (TCM), has the potential for early-stage disease screening. This study aimed to evaluate the effectiveness of deep learning-based analysis of tongue images for hepatic fibrosis screening.
Methods
A total of 1083 tongue images were collected from 741 patients and divided into training, validation, and test sets. DenseNet-201, a convolutional neural network, was employed to train the AI model using these tongue images. The predictive performance of AI was assessed and compared with that of FIB-4, using real-time two-dimensional shear wave elastography as the reference standard.
Results
The proposed AI model achieved an accuracy of 0.845 (95% CI: 0.79–0.90) and 0.814 (95% CI: 0.76–0.87) in the validation and test sets, respectively, with negative predictive values (NPVs) exceeding 90% in both sets. The AI model outperformed FIB-4 in all aspects, and when combined with FIB-4, the NPV reached 94.4%.
Conclusion
Tongue inspection, with the assistance of AI, could serve as a first-line screening method for hepatic fibrosis.
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