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Artificial intelligence in gastrointestinal endoscopy: The future is almost here.

作者信息

Alagappan Muthuraman, Brown Jeremy R Glissen, Mori Yuichi, Berzin Tyler M

机构信息

Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center, Harvard Medical, Boston, MA 02215, United States.

Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan.

出版信息

World J Gastrointest Endosc. 2018 Oct 16;10(10):239-249. doi: 10.4253/wjge.v10.i10.239.


DOI:10.4253/wjge.v10.i10.239
PMID:30364792
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6198310/
Abstract

Artificial intelligence (AI) enables machines to provide unparalleled value in a myriad of industries and applications. In recent years, researchers have harnessed artificial intelligence to analyze large-volume, unstructured medical data and perform clinical tasks, such as the identification of diabetic retinopathy or the diagnosis of cutaneous malignancies. Applications of artificial intelligence techniques, specifically machine learning and more recently deep learning, are beginning to emerge in gastrointestinal endoscopy. The most promising of these efforts have been in computer-aided detection and computer-aided diagnosis of colorectal polyps, with recent systems demonstrating high sensitivity and accuracy even when compared to expert human endoscopists. AI has also been utilized to identify gastrointestinal bleeding, to detect areas of inflammation, and even to diagnose certain gastrointestinal infections. Future work in the field should concentrate on creating seamless integration of AI systems with current endoscopy platforms and electronic medical records, developing training modules to teach clinicians how to use AI tools, and determining the best means for regulation and approval of new AI technology.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3496/6198310/b103d6092fef/WJGE-10-239-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3496/6198310/582187ef1c27/WJGE-10-239-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3496/6198310/e5bb76cfe32a/WJGE-10-239-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3496/6198310/b103d6092fef/WJGE-10-239-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3496/6198310/582187ef1c27/WJGE-10-239-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3496/6198310/e5bb76cfe32a/WJGE-10-239-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3496/6198310/b103d6092fef/WJGE-10-239-g003.jpg

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本文引用的文献

[1]
Artificial Intelligence-Assisted Polyp Detection for Colonoscopy: Initial Experience.

Gastroenterology. 2018-6

[2]
Hookworm Detection in Wireless Capsule Endoscopy Images With Deep Learning.

IEEE Trans Image Process. 2018-5

[3]
Deep learning analyzes Helicobacter pylori infection by upper gastrointestinal endoscopy images.

Endosc Int Open. 2018-2

[4]
Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images.

Gastric Cancer. 2018-1-15

[5]
Computer-Aided Diagnosis Based on Convolutional Neural Network System for Colorectal Polyp Classification: Preliminary Experience.

Oncology. 2017

[6]
Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes.

JAMA. 2017-12-12

[7]
Will Computer-Aided Detection and Diagnosis Revolutionize Colonoscopy?

Gastroenterology. 2017-12

[8]
Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model.

Gut. 2017-10-24

[9]
Mastering the game of Go without human knowledge.

Nature. 2017-10-18

[10]
Automated Classification of Benign and Malignant Proliferative Breast Lesions.

Sci Rep. 2017-8-29

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