In this study, the investigators proposed a prospective study about the effectiveness of speech and image recognition-based system in improving reporting quality during colonoscopy for colonoscopy report quality in endoscopists. The participants would be divided into two groups. For the collected colonoscopy videos, group A would record their observations with the assistance of the artificial intelligence system. The artificial intelligence assistant system can automatically capture bowel segment images and prompt abnormal lesions. Group B would complete the endoscopy report without special prompts. After a period of washout period, the two groups switched, that is, group A without AI assistance and group B with AI assistance to complete the colonoscopy report. Then, the completeness of the colonoscopy report, the completeness of capturing anatomical landmarks and detected lesions, the completeness of structured description, the accuracy of lesion reporting, the time for reporting and the satisfaction with the reporting system are compared with or without AI assistance.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
DEVICE_FEASIBILITY
Masking
NONE
Enrollment
10
The artificial intelligence assistant system can automatically capture bowel segment images and prompt abnormal lesions based on speech recognition and deep learning.
Renmin Hospital of Wuhan Univercity
Wuhan, Hubei, China
The integrity of colonoscopy report
Report integrity with or without AI-assisted. Calculation method = number of information recorded / total number of information need to record x 100%
Time frame: One month
The integrity of capturing anatomical landmarks
The integrity in captured bowel landmrak images with or without AI-assisted. Calculation method = number of anatomical landmarks in captured images / total number of anatomical landmarks x 100%
Time frame: One month
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