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Comparison of the Ability of Artificial-Intelligence-Based Computer-Aided Detection (CAD) Systems and Endoscopists to Detect Colorectal Neoplastic Lesions on Endoscopy Video

Artificial-intelligence-based computer-aided diagnosis (CAD) systems have developed remarkably in recent years. These systems can help increase the adenoma detection rate (ADR), an important quality indicator in colonoscopies. While there have been many still-image-based studies on the usefulness of...

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Autores principales: Misumi, Yoshitsugu, Nonaka, Kouichi, Takeuchi, Miharu, Kamitani, Yu, Uechi, Yasuhiro, Watanabe, Mai, Kishino, Maiko, Omori, Teppei, Yonezawa, Maria, Isomoto, Hajime, Tokushige, Katsutoshi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10381252/
https://www.ncbi.nlm.nih.gov/pubmed/37510955
http://dx.doi.org/10.3390/jcm12144840
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author Misumi, Yoshitsugu
Nonaka, Kouichi
Takeuchi, Miharu
Kamitani, Yu
Uechi, Yasuhiro
Watanabe, Mai
Kishino, Maiko
Omori, Teppei
Yonezawa, Maria
Isomoto, Hajime
Tokushige, Katsutoshi
author_facet Misumi, Yoshitsugu
Nonaka, Kouichi
Takeuchi, Miharu
Kamitani, Yu
Uechi, Yasuhiro
Watanabe, Mai
Kishino, Maiko
Omori, Teppei
Yonezawa, Maria
Isomoto, Hajime
Tokushige, Katsutoshi
author_sort Misumi, Yoshitsugu
collection PubMed
description Artificial-intelligence-based computer-aided diagnosis (CAD) systems have developed remarkably in recent years. These systems can help increase the adenoma detection rate (ADR), an important quality indicator in colonoscopies. While there have been many still-image-based studies on the usefulness of CAD, few have reported on its usefulness using actual clinical videos. However, no studies have compared the CAD group and control groups using the exact same case videos. This study aimed to determine whether CAD or endoscopists were superior in identifying colorectal neoplastic lesions in videos. In this study, we examined 34 lesions from 21 cases. CAD performed better than four of the six endoscopists (three experts and three beginners), including all the beginners. The time to lesion detection with beginners and experts was 2.147 ± 1.118 s and 1.394 ± 0.805 s, respectively, with significant differences between beginners and experts (p < 0.001) and between beginners and CAD (both p < 0.001). The time to lesion detection was significantly shorter for experts and CAD than for beginners. No significant difference was found between experts and CAD (p = 1.000). CAD could be useful as a diagnostic support tool for beginners to bridge the experience gap with experts.
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spelling pubmed-103812522023-07-29 Comparison of the Ability of Artificial-Intelligence-Based Computer-Aided Detection (CAD) Systems and Endoscopists to Detect Colorectal Neoplastic Lesions on Endoscopy Video Misumi, Yoshitsugu Nonaka, Kouichi Takeuchi, Miharu Kamitani, Yu Uechi, Yasuhiro Watanabe, Mai Kishino, Maiko Omori, Teppei Yonezawa, Maria Isomoto, Hajime Tokushige, Katsutoshi J Clin Med Article Artificial-intelligence-based computer-aided diagnosis (CAD) systems have developed remarkably in recent years. These systems can help increase the adenoma detection rate (ADR), an important quality indicator in colonoscopies. While there have been many still-image-based studies on the usefulness of CAD, few have reported on its usefulness using actual clinical videos. However, no studies have compared the CAD group and control groups using the exact same case videos. This study aimed to determine whether CAD or endoscopists were superior in identifying colorectal neoplastic lesions in videos. In this study, we examined 34 lesions from 21 cases. CAD performed better than four of the six endoscopists (three experts and three beginners), including all the beginners. The time to lesion detection with beginners and experts was 2.147 ± 1.118 s and 1.394 ± 0.805 s, respectively, with significant differences between beginners and experts (p < 0.001) and between beginners and CAD (both p < 0.001). The time to lesion detection was significantly shorter for experts and CAD than for beginners. No significant difference was found between experts and CAD (p = 1.000). CAD could be useful as a diagnostic support tool for beginners to bridge the experience gap with experts. MDPI 2023-07-22 /pmc/articles/PMC10381252/ /pubmed/37510955 http://dx.doi.org/10.3390/jcm12144840 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Misumi, Yoshitsugu
Nonaka, Kouichi
Takeuchi, Miharu
Kamitani, Yu
Uechi, Yasuhiro
Watanabe, Mai
Kishino, Maiko
Omori, Teppei
Yonezawa, Maria
Isomoto, Hajime
Tokushige, Katsutoshi
Comparison of the Ability of Artificial-Intelligence-Based Computer-Aided Detection (CAD) Systems and Endoscopists to Detect Colorectal Neoplastic Lesions on Endoscopy Video
title Comparison of the Ability of Artificial-Intelligence-Based Computer-Aided Detection (CAD) Systems and Endoscopists to Detect Colorectal Neoplastic Lesions on Endoscopy Video
title_full Comparison of the Ability of Artificial-Intelligence-Based Computer-Aided Detection (CAD) Systems and Endoscopists to Detect Colorectal Neoplastic Lesions on Endoscopy Video
title_fullStr Comparison of the Ability of Artificial-Intelligence-Based Computer-Aided Detection (CAD) Systems and Endoscopists to Detect Colorectal Neoplastic Lesions on Endoscopy Video
title_full_unstemmed Comparison of the Ability of Artificial-Intelligence-Based Computer-Aided Detection (CAD) Systems and Endoscopists to Detect Colorectal Neoplastic Lesions on Endoscopy Video
title_short Comparison of the Ability of Artificial-Intelligence-Based Computer-Aided Detection (CAD) Systems and Endoscopists to Detect Colorectal Neoplastic Lesions on Endoscopy Video
title_sort comparison of the ability of artificial-intelligence-based computer-aided detection (cad) systems and endoscopists to detect colorectal neoplastic lesions on endoscopy video
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10381252/
https://www.ncbi.nlm.nih.gov/pubmed/37510955
http://dx.doi.org/10.3390/jcm12144840
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