Application of artificial intelligence in endoscopic gastrointestinal tumors.

adenoma detection rate artificial intelligence colorectal cancer deep learning gastric cancer

Journal

Frontiers in oncology
ISSN: 2234-943X
Titre abrégé: Front Oncol
Pays: Switzerland
ID NLM: 101568867

Informations de publication

Date de publication:
2023
Historique:
received: 14 06 2023
accepted: 17 11 2023
medline: 25 12 2023
pubmed: 25 12 2023
entrez: 25 12 2023
Statut: epublish

Résumé

With an increasing number of patients with gastrointestinal cancer, effective and accurate early diagnostic clinical tools are required provide better health care for patients with gastrointestinal cancer. Recent studies have shown that artificial intelligence (AI) plays an important role in the diagnosis and treatment of patients with gastrointestinal tumors, which not only improves the efficiency of early tumor screening, but also significantly improves the survival rate of patients after treatment. With the aid of efficient learning and judgment abilities of AI, endoscopists can improve the accuracy of diagnosis and treatment through endoscopy and avoid incorrect descriptions or judgments of gastrointestinal lesions. The present article provides an overview of the application status of various artificial intelligence in gastric and colorectal cancers in recent years, and the direction of future research and clinical practice is clarified from a clinical perspective to provide a comprehensive theoretical basis for AI as a promising diagnostic and therapeutic tool for gastrointestinal cancer.

Identifiants

pubmed: 38144533
doi: 10.3389/fonc.2023.1239788
pmc: PMC10747923
doi:

Types de publication

Journal Article Review

Langues

eng

Pagination

1239788

Informations de copyright

Copyright © 2023 Xin, Zhang, Liu, Li, Mao and Li.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Auteurs

Yiping Xin (Y)

Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China.

Qi Zhang (Q)

Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China.

Xinyuan Liu (X)

Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China.

Bingqing Li (B)

Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China.

Tao Mao (T)

Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China.

Xiaoyu Li (X)

Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China.

Classifications MeSH