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Scoping out the future: The application of artificial intelligence to gastrointestinal endoscopy.

作者信息

Minchenberg Scott B, Walradt Trent, Glissen Brown Jeremy R

机构信息

Department of Internal Medicine, Beth Israel Deaconess Medical Center, Boston, MA 02130, United States.

Division of Gastroenterology, Beth Israel Deaconess Medical Center, Boston, MA 02130, United States.

出版信息

World J Gastrointest Oncol. 2022 May 15;14(5):989-1001. doi: 10.4251/wjgo.v14.i5.989.


DOI:10.4251/wjgo.v14.i5.989
PMID:35646286
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9124983/
Abstract

Artificial intelligence (AI) is a quickly expanding field in gastrointestinal endoscopy. Although there are a myriad of applications of AI ranging from identification of bleeding to predicting outcomes in patients with inflammatory bowel disease, a great deal of research has focused on the identification and classification of gastrointestinal malignancies. Several of the initial randomized, prospective trials utilizing AI in clinical medicine have centered on polyp detection during screening colonoscopy. In addition to work focused on colorectal cancer, AI systems have also been applied to gastric, esophageal, pancreatic, and liver cancers. Despite promising results in initial studies, the generalizability of most of these AI systems have not yet been evaluated. In this article we review recent developments in the field of AI applied to gastrointestinal oncology.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c5a3/9124983/39758d40e8bb/WJGO-14-989-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c5a3/9124983/062363a1e956/WJGO-14-989-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c5a3/9124983/39758d40e8bb/WJGO-14-989-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c5a3/9124983/062363a1e956/WJGO-14-989-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c5a3/9124983/39758d40e8bb/WJGO-14-989-g002.jpg

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本文引用的文献

[1]
The single-monitor trial: an embedded CADe system increased adenoma detection during colonoscopy: a prospective randomized study.

Therap Adv Gastroenterol. 2020-12-15

[2]
Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension.

Lancet Digit Health. 2020-10

[3]
Real-time computer aided colonoscopy versus standard colonoscopy for improving adenoma detection rate: A meta-analysis of randomized-controlled trials.

EClinicalMedicine. 2020-11-21

[4]
Utilisation of artificial intelligence for the development of an EUS-convolutional neural network model trained to enhance the diagnosis of autoimmune pancreatitis.

Gut. 2021-7

[5]
Artificial intelligence in gastric cancer: Application and future perspectives.

World J Gastroenterol. 2020-9-28

[6]
Efficacy of endoscopic ultrasound with artificial intelligence for the diagnosis of gastrointestinal stromal tumors.

J Gastroenterol. 2020-12

[7]
Application of artificial intelligence using a novel EUS-based convolutional neural network model to identify and distinguish benign and malignant hepatic masses.

Gastrointest Endosc. 2021-5

[8]
Using Computer-Aided Polyp Detection During Colonoscopy.

Am J Gastroenterol. 2020-7

[9]
Comparison of performances of artificial intelligence versus expert endoscopists for real-time assisted diagnosis of esophageal squamous cell carcinoma (with video).

Gastrointest Endosc. 2020-10

[10]
Automatic classification of esophageal lesions in endoscopic images using a convolutional neural network.

Ann Transl Med. 2020-4

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