Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
The goal of this clinic trial is to learn about the effect of AI monitoring blind spots on the inspection time to EGD. Patients are randomly assigned to undergo an EGD with or without the assistance of AI. In the AI group, except for the original videos, there is additional information presented to endoscopists:(1)the virtual stomach model monitoring;(2)time;(3)scoring. Researchers will compare intervention group to see if it have a shorter inspection time compared with the control group.
The goal of this clinic trial is to evaluate the effect of real-time artificial intelligence for monitoring blind spots on the inspection time of EGD. Patients are randomly assigned to undergo an EGD with or without the assistance of AI. In the AI group, except for the original videos, there is additional information presented to endoscopists:(1)the virtual stomach model monitoring;(2)time;(3)scoring. Researchers will compare intervention group to see if it have a shorter inspection time in the case of non-inferior detection rate of gastric neoplastic lesions.
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| AI group | Experimental | In the AI group, except for the original videos, there is additional information presented to endoscopists:(1)the virtual stomach model monitoring;(2)time;(3)scoring. Endoscopists will complete EGD examination without blind spots. |
|
| Routine group | No Intervention | In the Routine group, only the original videos and there is no additional information, and inspection time will be no less than 7 minutes. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Endoangle | Device | there is additional information presented to endoscopists:(1)the virtual stomach model monitoring;(2)time;(3)scoring. |
|
| Measure | Description | Time Frame |
|---|---|---|
| Dection rate of neoplastic lesions | Proportion of patients with neoplastic lesions among all patients undergoing esophagogastroduodenoscopy. | Through study completion, an average of 1 year |
| Measure | Description | Time Frame |
|---|---|---|
| Inspection time | It is the time from intubation to extubation of the patient without biopsy. | 20min |
Not provided
Inclusion Criteria:
Exclusion Criteria:
Not provided
Not provided
Not provided
Not provided
Not provided
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 30858305 | Background | Wu L, Zhang J, Zhou W, An P, Shen L, Liu J, Jiang X, Huang X, Mu G, Wan X, Lv X, Gao J, Cui N, Hu S, Chen Y, Hu X, Li J, Chen D, Gong D, He X, Ding Q, Zhu X, Li S, Wei X, Li X, Wang X, Zhou J, Zhang M, Yu HG. Randomised controlled trial of WISENSE, a real-time quality improving system for monitoring blind spots during esophagogastroduodenoscopy. Gut. 2019 Dec;68(12):2161-2169. doi: 10.1136/gutjnl-2018-317366. Epub 2019 Mar 11. | |
| 34547254 |
Not provided
Not provided
Sponsor approval for data sharing should be sought; Data access requests should be made via an application form detailing the specific requirements and the proposed research and publication plan
Not provided
Not provided
Not provided
Not provided
Not provided
| ID | Term |
|---|---|
| D012607 | Scotoma |
| ID | Term |
|---|---|
| D014786 | Vision Disorders |
| D012678 | Sensation Disorders |
| D009461 | Neurologic Manifestations |
| D009422 | Nervous System Diseases |
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Background |
| Wu L, Xu M, Jiang X, He X, Zhang H, Ai Y, Tong Q, Lv P, Lu B, Guo M, Huang M, Ye L, Shen L, Yu H. Real-time artificial intelligence for detecting focal lesions and diagnosing neoplasms of the stomach by white-light endoscopy (with videos). Gastrointest Endosc. 2022 Feb;95(2):269-280.e6. doi: 10.1016/j.gie.2021.09.017. Epub 2021 Sep 20. |
| 27548885 | Background | Bisschops R, Areia M, Coron E, Dobru D, Kaskas B, Kuvaev R, Pech O, Ragunath K, Weusten B, Familiari P, Domagk D, Valori R, Kaminski MF, Spada C, Bretthauer M, Bennett C, Senore C, Dinis-Ribeiro M, Rutter MD. Performance measures for upper gastrointestinal endoscopy: a European Society of Gastrointestinal Endoscopy (ESGE) Quality Improvement Initiative. Endoscopy. 2016 Sep;48(9):843-64. doi: 10.1055/s-0042-113128. Epub 2016 Aug 22. No abstract available. |
| 40125855 | Derived | Tan X, Yao L, Dong Z, Li Y, Yu Y, Gao X, Zhu K, Su W, Yin H, Wang W, Luo C, Li J, You H, Hu H, Zhou W, Yu H. Artificial Intelligence as a Surrogate for Inspection Time to Assess Completeness in Esophagogastroduodenoscopy: A Prospective, Randomized, Noninferiority Study. Clin Transl Gastroenterol. 2025 Mar 25;16(6):e00839. doi: 10.14309/ctg.0000000000000839. eCollection 2025 Jun 1. |
| D005128 |
| Eye Diseases |
| D012816 | Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |