Artificial intelligence in theranostics of gastric cancer, a review-Reference-Cited by-同舟云学术

Artificial intelligence in theranostics of gastric cancer, a review

Published:2023-06-01 Issue:3 Volume:3 Page:214-229
ISSN:2749-9642
Container-title:Medical Review
language:en
Short-container-title:

Author:

Zha Yiqian¹²,Xue Cuili¹²,Liu Yanlei¹²,Ni Jian¹²,De La Fuente Jesus M.³,Cui Daxiang¹²^ORCID

Affiliation:

1. Institute of Nano Biomedicine and Engineering, Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment Instrument, School of Sensing Science and Engineering, Shanghai Jiao Tong University , Shanghai , China

2. National Engineering Research Center for Nanotechnology , Shanghai , China

3. Institute of Aragon Nanoscience, University of Zaragoza , Zaragoza , Spain

Abstract

Abstract Gastric cancer (GC) is one of the commonest cancers with high morbidity and mortality in the world. How to realize precise diagnosis and therapy of GC owns great clinical requirement. In recent years, artificial intelligence (AI) has been actively explored to apply to early diagnosis and treatment and prognosis of gastric carcinoma. Herein, we review recent advance of AI in early screening, diagnosis, therapy and prognosis of stomach carcinoma. Especially AI combined with breath screening early GC system improved 97.4 % of early GC diagnosis ratio, AI model on stomach cancer diagnosis system of saliva biomarkers obtained an overall accuracy of 97.18 %, specificity of 97.44 %, and sensitivity of 96.88 %. We also discuss concept, issues, approaches and challenges of AI applied in stomach cancer. This review provides a comprehensive view and roadmap for readers working in this field, with the aim of pushing application of AI in theranostics of stomach cancer to increase the early discovery ratio and curative ratio of GC patients.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Walter de Gruyter GmbH

Link

https://www.degruyter.com/document/doi/10.1515/mr-2022-0042/pdf

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