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| Name | Class |
|---|---|
| Ancon Technologies Ltd | INDUSTRY |
| Shandong Provincial Hospital | OTHER_GOV |
| Binzhou Medical University | OTHER |
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With the rapid development of artificial intelligence technology, more and more deep learning technology has been applied to medicine. Our research is to develop a set of quality control system for magnetic capsule gastroscope using deep learning technology, and conduct a randomized controlled trial to verify its practical efficiency.
The accuracy of magnetic controlled capsule endoscopy(MCCE) in the diagnosis of stomach lesions is highly consistent with the traditional electronic gastroscopy. It has become a new comfortable and safe form for screening and It is also the beneficial complementarity of the traditional electronic gastroscope. To ensure the medical quality of magnetic controlled capsule endoscopy system, Based on Artificial Intelligence Deep Learning Technology ,investigators developed the Magnetic-controlled Capsule Endoscopic Assisted Quality Control System(AI-box).Randomized controlled trials will be conducted on prospective subjects to verify the quality control efficiency.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Participates being subjected to capsule endoscopy (AI-box assistant) | Experimental | In this group ,Participates will be subjected to magnetically controlled capsule gastroscopy with AI-box assistant. |
|
| Participates being subjected to capsule endoscopy | No Intervention | In this group ,Participates will be subjected to magnetically controlled capsule gastroscopy without AI-box assistant. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| AI-box | Other | Automatic quality-control system(AI-box)could real-time measure endoscopic inspection completeness, evaluate gastric cleanliness. |
|
| Measure | Description | Time Frame |
|---|---|---|
| Mean inspection completeness | After the inspection,investigators will review the whole process of inspection and record the sites observed as well as unobserved sites,then calculate the inspection completeness(number of observed sites in each patient/10)besides the the blind spot rate(number of unobserved sites in each patient/10 ) per procedure in control group and AI-assisted group. | 4 months |
| Measure | Description | Time Frame |
|---|---|---|
| Cleanliness judgement consistency between expert and AI-box | After the inspection,investigators will review the whole process of inspection and make a judgement of cleanliness of different sites of gastric then compared with the cleanliness judgement of AI-box. So at last investigators will calculate the accuracy of the AI result. | 4 months |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Yanqing Li, PhD | Qilu Hospital of Shandong University | Study Chair |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Qilu Hospital of Shandong University | Jinan | Shandong | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 42069608 | Derived | Wang X, Shen D, Zhao Y, Xing J, Liu J, Zhou R, Yang C, Liu C, Ma X, Ge J, Zhang H, Yuan W, Zhang H, Ma Y, Hu P, Zuo X, Li Y, Li Z. Impact of a real-time automatic quality control system for magnetically controlled capsule gastroscopy: a multicenter randomized controlled trial. BMC Med. 2026 May 2;24(1):369. doi: 10.1186/s12916-026-04901-0. |
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Both participates and outcomes Assesors will be masked