| Autor: |
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 Lu |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Diagnostics, Vol 12, Iss 3, p 613 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2075-4418 |
| DOI: |
10.3390/diagnostics12030613 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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