Use of U-Net Convolutional Neural Networks for Automated Segmentation of Fecal Material for Objective Evaluation of Bowel Preparation Quality in Colonoscopy Article Swipe
YOU?
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· 2022
· Open Access
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· DOI: https://doi.org/10.3390/diagnostics12030613
Background: Adequate bowel cleansing is important for colonoscopy performance evaluation. Current bowel cleansing evaluation scales are subjective, with a wide variation in consistency among physicians and low reported rates of accuracy. We aim to use machine learning to develop a fully automatic segmentation method for the objective evaluation of the adequacy of colon preparation. Methods: Colonoscopy videos were retrieved from a video data cohort and transferred to qualified images, which were randomly divided into training, validation, and verification datasets. The fecal residue was manually segmented. A deep learning model based on the U-Net convolutional network architecture was developed to perform automatic segmentation. The performance of the automatic segmentation was evaluated on the overlap area with the manual segmentation. Results: A total of 10,118 qualified images from 119 videos were obtained. The model averaged 0.3634 s to segmentate one image automatically. The models produced a strong high-overlap area with manual segmentation, with 94.7% ± 0.67% of that area predicted by our AI model, which correlated well with the area measured manually (r = 0.915, p < 0.001). The AI system can be applied in real-time qualitatively and quantitatively. Conclusions: We established a fully automatic segmentation method to rapidly and accurately mark the fecal residue-coated mucosa for the objective evaluation of colon preparation.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/diagnostics12030613
- https://www.mdpi.com/2075-4418/12/3/613/pdf?version=1646184076
- OA Status
- gold
- Cited By
- 13
- References
- 53
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4214623706
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4214623706Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/diagnostics12030613Digital Object Identifier
- Title
-
Use of U-Net Convolutional Neural Networks for Automated Segmentation of Fecal Material for Objective Evaluation of Bowel Preparation Quality in ColonoscopyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-03-01Full publication date if available
- Authors
-
Yen‐Po Wang, Ying-Chun Jheng, Kuang‐Yi Sung, Hung-En Lin, I‐Fang Hsin, Ping‐Hsien Chen, Yuan-Chia Chu, David Lu, Yuan‐Jen Wang, Ming‐Chih Hou, Fa‐Yauh Lee, Ching‐Liang LuList of authors in order
- Landing page
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https://doi.org/10.3390/diagnostics12030613Publisher landing page
- PDF URL
-
https://www.mdpi.com/2075-4418/12/3/613/pdf?version=1646184076Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2075-4418/12/3/613/pdf?version=1646184076Direct OA link when available
- Concepts
-
Segmentation, Artificial intelligence, Computer science, Convolutional neural network, Colonoscopy, Consistency (knowledge bases), Image segmentation, Deep learning, Pattern recognition (psychology), Computer vision, Medicine, Colorectal cancer, Internal medicine, CancerTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
13Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 5, 2023: 2, 2022: 4Per-year citation counts (last 5 years)
- References (count)
-
53Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| primary_location.raw_source_name | Diagnostics |
| primary_location.landing_page_url | https://doi.org/10.3390/diagnostics12030613 |
| publication_date | 2022-03-01 |
| publication_year | 2022 |
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