In this study, we proposed a prospective study about the effectiveness of artificial intelligence system for endoscopy report quality in endoscopists. The subjects would be divided into two groups. For the collected endoscopic videos, group A would complete the endoscopy report with the assistance of the artificial intelligence system. The artificial intelligence assistant system can automatically capture images, prompt abnormal lesions and the parts covered by the examination (the upper gastrointestinal tract is divided into 26 parts). Group B would complete the endoscopy report without special prompts. After a period of forgetting, the two groups switched, that is, group A without AI assistance and group B with AI assistance to complete the endoscopy report. Then, the completeness of the report lesion, the accuracy of the lesion location, the completeness of the lesion and the standard part in the captured images, and so on were 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 images, prompt abnormal lesions and the parts covered by the examination (the stomach is divided into 26 parts).
Renmin Hospital of Wuhan University
Wuhan, China
RECRUITINGIntegrity of report lesion
Report lesion integrity with or without AI-assisted. Calculation method = number of report lesions / total number of lesions x 100%
Time frame: one month
Accuracy of lesion location
Accuracy of lesion location with or without AI-assisted. Calculation method = number of lesion with correct location / total number of lesions x 100%
Time frame: one month
Integrity of lesion in captured images
Lesion integrity in captured images with or without AI-assisted. Calculation method = number of lesions in captured images / total number of lesions x 100%
Time frame: one month
Integrity of standard part in captured images
Lesion integrity in captured images with or without AI-assisted. Calculation method = number of standard parts in captured images / the actual number of standard parts covered by the examination x 100%
Time frame: one month
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