We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Use of U-Net Convolutional Neural Networks for Automated Segmentation of Fecal Material for Objective Evaluation of Bowel Preparation Quality in Colonoscopy.
- Authors
Wang, Yen-Po; Jheng, Ying-Chun; Sung, Kuang-Yi; Lin, Hung-En; Hsin, I-Fang; Chen, Ping-Hsien; Chu, Yuan-Chia; Lu, David; Wang, Yuan-Jen; Hou, Ming-Chih; Lee, Fa-Yauh; Lu, Ching-Liang
- Abstract
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.
- Subjects
CONVOLUTIONAL neural networks; FECES; COLONOSCOPY; DEEP learning; MACHINE learning
- Publication
Diagnostics (2075-4418), 2022, Vol 12, Issue 3, p613
- ISSN
2075-4418
- Publication type
Article
- DOI
10.3390/diagnostics12030613