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

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 an...

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Detalles Bibliográficos
Autores principales: Liu, Qi, Li, Yan, Yang, Peng, Liu, Quanquan, Wang, Chunbao, Chen, Keji, Wu, Zhengzhi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10408356/
https://www.ncbi.nlm.nih.gov/pubmed/37559828
http://dx.doi.org/10.1177/20552076231191044
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author Liu, Qi
Li, Yan
Yang, Peng
Liu, Quanquan
Wang, Chunbao
Chen, Keji
Wu, Zhengzhi
author_facet Liu, Qi
Li, Yan
Yang, Peng
Liu, Quanquan
Wang, Chunbao
Chen, Keji
Wu, Zhengzhi
author_sort Liu, Qi
collection PubMed
description 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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spelling pubmed-104083562023-08-09 A survey of artificial intelligence in tongue image for disease diagnosis and syndrome differentiation Liu, Qi Li, Yan Yang, Peng Liu, Quanquan Wang, Chunbao Chen, Keji Wu, Zhengzhi Digit Health Review Article 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. SAGE Publications 2023-08-06 /pmc/articles/PMC10408356/ /pubmed/37559828 http://dx.doi.org/10.1177/20552076231191044 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by-nc-nd/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License (https://creativecommons.org/licenses/by-nc-nd/4.0/) which permits non-commercial use, reproduction and distribution of the work as published without adaptation or alteration, without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Review Article
Liu, Qi
Li, Yan
Yang, Peng
Liu, Quanquan
Wang, Chunbao
Chen, Keji
Wu, Zhengzhi
A survey of artificial intelligence in tongue image for disease diagnosis and syndrome differentiation
title A survey of artificial intelligence in tongue image for disease diagnosis and syndrome differentiation
title_full A survey of artificial intelligence in tongue image for disease diagnosis and syndrome differentiation
title_fullStr A survey of artificial intelligence in tongue image for disease diagnosis and syndrome differentiation
title_full_unstemmed A survey of artificial intelligence in tongue image for disease diagnosis and syndrome differentiation
title_short A survey of artificial intelligence in tongue image for disease diagnosis and syndrome differentiation
title_sort survey of artificial intelligence in tongue image for disease diagnosis and syndrome differentiation
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10408356/
https://www.ncbi.nlm.nih.gov/pubmed/37559828
http://dx.doi.org/10.1177/20552076231191044
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