Development of a deep learning-based software for calculating cleansing score in small bowel capsule endoscopy

Abstract A standardized small bowel (SB) cleansing scale is currently not available. The aim of this study was to develop an automated calculation software for SB cleansing score using deep learning. Consecutively performed capsule endoscopy cases were enrolled from three hospitals. A 5-step scoring...

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Autores principales:	Ji Hyung Nam, Youngbae Hwang, Dong Jun Oh, Junseok Park, Ki Bae Kim, Min Kyu Jung, Yun Jeong Lim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7a703ea487d14aee9fd2026beac90b2e
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spelling	oai:doaj.org-article:7a703ea487d14aee9fd2026beac90b2e2021-12-02T13:19:23ZDevelopment of a deep learning-based software for calculating cleansing score in small bowel capsule endoscopy10.1038/s41598-021-81686-72045-2322https://doaj.org/article/7a703ea487d14aee9fd2026beac90b2e2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81686-7https://doaj.org/toc/2045-2322Abstract A standardized small bowel (SB) cleansing scale is currently not available. The aim of this study was to develop an automated calculation software for SB cleansing score using deep learning. Consecutively performed capsule endoscopy cases were enrolled from three hospitals. A 5-step scoring system based on mucosal visibility was trained for deep learning in the training set. Performance of the trained software was evaluated in the validation set. Average cleansing score (1.0 to 5.0) by deep learning was compared to clinical grading (A to C) reviewed by clinicians. Cleansing scores decreased as clinical grading worsened (scores of 4.1, 3.5, and 2.9 for grades A, B, and C, respectively, P < 0.001). Adequate preparation was achieved for 91.7% of validation cases. The average cleansing score was significantly different between adequate and inadequate group (4.0 vs. 2.9, P < 0.001). ROC curve analysis revealed that a cut-off value of cleansing score at 3.25 had an AUC of 0.977. Diagnostic yields for small, hard-to-find lesions were associated with high cleansing scores (4.3 vs. 3.8, P < 0.001). We developed a novel scoring software which calculates objective, automated cleansing scores for SB preparation. The cut-off value we suggested provides a standard criterion for adequate bowel preparation as a quality indicator.Ji Hyung NamYoungbae HwangDong Jun OhJunseok ParkKi Bae KimMin Kyu JungYun Jeong LimNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ji Hyung Nam Youngbae Hwang Dong Jun Oh Junseok Park Ki Bae Kim Min Kyu Jung Yun Jeong Lim Development of a deep learning-based software for calculating cleansing score in small bowel capsule endoscopy
description	Abstract A standardized small bowel (SB) cleansing scale is currently not available. The aim of this study was to develop an automated calculation software for SB cleansing score using deep learning. Consecutively performed capsule endoscopy cases were enrolled from three hospitals. A 5-step scoring system based on mucosal visibility was trained for deep learning in the training set. Performance of the trained software was evaluated in the validation set. Average cleansing score (1.0 to 5.0) by deep learning was compared to clinical grading (A to C) reviewed by clinicians. Cleansing scores decreased as clinical grading worsened (scores of 4.1, 3.5, and 2.9 for grades A, B, and C, respectively, P < 0.001). Adequate preparation was achieved for 91.7% of validation cases. The average cleansing score was significantly different between adequate and inadequate group (4.0 vs. 2.9, P < 0.001). ROC curve analysis revealed that a cut-off value of cleansing score at 3.25 had an AUC of 0.977. Diagnostic yields for small, hard-to-find lesions were associated with high cleansing scores (4.3 vs. 3.8, P < 0.001). We developed a novel scoring software which calculates objective, automated cleansing scores for SB preparation. The cut-off value we suggested provides a standard criterion for adequate bowel preparation as a quality indicator.
format	article
author	Ji Hyung Nam Youngbae Hwang Dong Jun Oh Junseok Park Ki Bae Kim Min Kyu Jung Yun Jeong Lim
author_facet	Ji Hyung Nam Youngbae Hwang Dong Jun Oh Junseok Park Ki Bae Kim Min Kyu Jung Yun Jeong Lim
author_sort	Ji Hyung Nam
title	Development of a deep learning-based software for calculating cleansing score in small bowel capsule endoscopy
title_short	Development of a deep learning-based software for calculating cleansing score in small bowel capsule endoscopy
title_full	Development of a deep learning-based software for calculating cleansing score in small bowel capsule endoscopy
title_fullStr	Development of a deep learning-based software for calculating cleansing score in small bowel capsule endoscopy
title_full_unstemmed	Development of a deep learning-based software for calculating cleansing score in small bowel capsule endoscopy
title_sort	development of a deep learning-based software for calculating cleansing score in small bowel capsule endoscopy
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7a703ea487d14aee9fd2026beac90b2e
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Development of a deep learning-based software for calculating cleansing score in small bowel capsule endoscopy

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