A current and newly proposed artificial intelligence algorithm for reading small bowel capsule endoscopy

Dong Jun Oh; Youngbae Hwang; Yun Jeong Lim

doi:10.3390/diagnostics11071183

A current and newly proposed artificial intelligence algorithm for reading small bowel capsule endoscopy

Dong Jun Oh, Youngbae Hwang, Yun Jeong Lim

Department of Medicine

Chungbuk National University

Research output: Contribution to journal › Review article › peer-review

9 Scopus citations

Abstract

Small bowel capsule endoscopy (SBCE) is one of the most useful methods for diagnosing small bowel mucosal lesions. However, it takes a long time to interpret the capsule images. To solve this problem, artificial intelligence (AI) algorithms for SBCE readings are being actively studied. In this article, we analyzed several studies that applied AI algorithms to SBCE readings, such as automatic lesion detection, automatic classification of bowel cleanliness, and automatic compartmentalization of small bowels. In addition to automatic lesion detection using AI algorithms, a new direction of AI algorithms related to shorter reading times and improved lesion detection accuracy should be considered. Therefore, it is necessary to develop an integrated AI algorithm composed of algorithms with various functions in order to be used in clinical practice.

Original language	English
Article number	1183
Journal	Diagnostics
Volume	11
Issue number	7
DOIs	https://doi.org/10.3390/diagnostics11071183
State	Published - Jul 2021

Keywords

Artificial intelligence
Automatic detection
Capsule endoscopy
Reading software

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10.3390/diagnostics11071183

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title = "A current and newly proposed artificial intelligence algorithm for reading small bowel capsule endoscopy",

abstract = "Small bowel capsule endoscopy (SBCE) is one of the most useful methods for diagnosing small bowel mucosal lesions. However, it takes a long time to interpret the capsule images. To solve this problem, artificial intelligence (AI) algorithms for SBCE readings are being actively studied. In this article, we analyzed several studies that applied AI algorithms to SBCE readings, such as automatic lesion detection, automatic classification of bowel cleanliness, and automatic compartmentalization of small bowels. In addition to automatic lesion detection using AI algorithms, a new direction of AI algorithms related to shorter reading times and improved lesion detection accuracy should be considered. Therefore, it is necessary to develop an integrated AI algorithm composed of algorithms with various functions in order to be used in clinical practice.",

keywords = "Artificial intelligence, Automatic detection, Capsule endoscopy, Reading software",

author = "Oh, {Dong Jun} and Youngbae Hwang and Lim, {Yun Jeong}",

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doi = "10.3390/diagnostics11071183",

language = "English",

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journal = "Diagnostics",

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T1 - A current and newly proposed artificial intelligence algorithm for reading small bowel capsule endoscopy

AU - Oh, Dong Jun

AU - Hwang, Youngbae

AU - Lim, Yun Jeong

PY - 2021/7

Y1 - 2021/7

N2 - Small bowel capsule endoscopy (SBCE) is one of the most useful methods for diagnosing small bowel mucosal lesions. However, it takes a long time to interpret the capsule images. To solve this problem, artificial intelligence (AI) algorithms for SBCE readings are being actively studied. In this article, we analyzed several studies that applied AI algorithms to SBCE readings, such as automatic lesion detection, automatic classification of bowel cleanliness, and automatic compartmentalization of small bowels. In addition to automatic lesion detection using AI algorithms, a new direction of AI algorithms related to shorter reading times and improved lesion detection accuracy should be considered. Therefore, it is necessary to develop an integrated AI algorithm composed of algorithms with various functions in order to be used in clinical practice.

AB - Small bowel capsule endoscopy (SBCE) is one of the most useful methods for diagnosing small bowel mucosal lesions. However, it takes a long time to interpret the capsule images. To solve this problem, artificial intelligence (AI) algorithms for SBCE readings are being actively studied. In this article, we analyzed several studies that applied AI algorithms to SBCE readings, such as automatic lesion detection, automatic classification of bowel cleanliness, and automatic compartmentalization of small bowels. In addition to automatic lesion detection using AI algorithms, a new direction of AI algorithms related to shorter reading times and improved lesion detection accuracy should be considered. Therefore, it is necessary to develop an integrated AI algorithm composed of algorithms with various functions in order to be used in clinical practice.

KW - Artificial intelligence

KW - Automatic detection

KW - Capsule endoscopy

KW - Reading software

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U2 - 10.3390/diagnostics11071183

DO - 10.3390/diagnostics11071183

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AN - SCOPUS:85109844953

SN - 2075-4418

VL - 11

JO - Diagnostics

JF - Diagnostics

IS - 7

M1 - 1183

ER -

A current and newly proposed artificial intelligence algorithm for reading small bowel capsule endoscopy

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Keywords

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