Previously submitted to: Journal of Medical Internet Research (no longer under consideration since Dec 16, 2021)
Date Submitted: Apr 2, 2021
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Use of U-Net Convolutional Neural Networks for Automated Segmentation of Fecal Material to Objective Evaluation of Bowel Preparation Quality in Colonoscopy
ABSTRACT
Background:
Adequate bowel cleansing is important for a complete examination of the colon mucosa during colonoscopy. Current bowel cleansing evaluation scales are subjective with a wide variation in consistency among physicians and low reported rate. Artificial intelligence (AI) has been increasingly used in endoscopy.
Objective:
We aim to use machine learning to develop a fully automatic segmentation method to mark the fecal residue-coated mucosa for 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 by skilled technicians. Deep learning model based on the U-Net convolutional network architecture was developed to perform automatic segmentation. TheA total of 10,118 qualified images from 119 videos were captured, and labelled manually. The model averaged 0.3634 seconds to segmentate one image automatically. The models produced a strong high-overlap area with manual segmentation to 94.7% ± 0.67% with an intersection over union (IOU) of 0.607 ± 0.17. The area predicted by our AI model correlated well with the area measured manually (r=0.915, p<0.001). The AI system can be applied real-time to qualitatively and quantitatively display the mucosa covered by fecal residue. 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 captured, and labelled manually. The model averaged 0.3634 seconds to segmentate one image automatically. The models produced a strong high-overlap area with manual segmentation to 94.7% ± 0.67% with an intersection over union (IOU) of 0.607 ± 0.17. The area predicted by our AI model correlated well with the area measured manually (r=0.915, p<0.001). The AI system can be applied real-time to qualitatively and quantitatively display the mucosa covered by fecal residue.
Conclusions:
We used machine learning to establish a fully automatic segmentation method to rapidly and accurately mark the fecal residue-coated mucosa for objective evaluation of colon preparation.
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