Use of U-Net Convolutional Neural Networks for Automated Segmentation of Fecal Material for Objective Evaluation of Bowel Preparation Quality in Colonoscopy

Exploring foci of: Diagnostics • Vol 12 • No 3 Use of U-Net Convolutional Neural Networks for Automated Segmentation of Fecal Material for Objective Evaluation of Bowel Preparation Quality in Colonoscopy March 2022 • 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‐Yau… 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 i… Open Article Page

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