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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| with Artificial intelligence assistant system | Experimental | Endoscopists would complete the colonoscopy report with the assistance of the artificial intelligence system. |
|
| without Artificial intelligence assistant system | No Intervention | Endoscopists would complete the colonoscopy report without special prompts. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Artificial intelligence assistant system | Diagnostic Test | The artificial intelligence assistant system can automatically capture bowel segment images and prompt abnormal lesions based on speech recognition and deep learning. |
| Measure | Description | Time Frame |
|---|---|---|
| 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% | One month |
| Measure | Description | Time Frame |
|---|---|---|
| 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% | One month |
| Measure | Description | Time Frame |
|---|---|---|
| The integrity of report lesion | Report lesion integrity with or without AI-assisted. Calculation method = number of report lesions / total number of lesions x 100% | One month |
| The completeness of structured description |
Inclusion Criteria:
Patients:
Doctors:
Exclusion Criteria:
Patients:
Doctors:
1. The researcher believes that the subjects are not suitable for participating in clinical trials.
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Honggang Yu, MD | Contact | 13871281899 | yuhonggang1969@163.com |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Renmin Hospital of Wuhan Univercity | Wuhan | Hubei | 430060 | China |
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The completeness of structured description with or without AI-assisted. Calculation method = number of structured descriptions / total number of structured descriptions need to record x 100%
| One month |
| Accuracy of lesion reporting | Accuracy of lesion report with or without AI-assisted. Calculation method = number of lesions with correct description / total number of lesions descriptionx 100% | One month |
| The time for reporting | The time for reporting with or without AI-assisted | One month |
| The satisfaction with the reporting system | The satisfaction with the reporting system with or without AI-assisted | One month |