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This study aimed to find the CDSS-assisted upper GI endoscopy has clinical benefit compared to conventional routine screening upper GI endoscopy. Investigators will conduct a pilot randomized controlled study. Consecutive patients will be allocated either to CDSS-assisted upper GI endoscopy or conventional routine screening upper GI endoscopy. The lesion detection rate will be compared between both groups.
Artificial intelligence has been adopted in the field of gastrointestinal endoscopy. Investigators previously established deep-learning models to predict the histology and invasion depth of gastric lesions using endoscopic stillcut images. However, clinical benefit of this model has not been evaluated. This study aimed to find the CDSS-assisted upper GI endoscopy has clinical benefit compared to conventional routine screening upper GI endoscopy. Investigators will conduct a pilot randomized controlled study. Consecutive patients who visited Chuncheon Sacred Heart hospital will be allocated either to CDSS-assisted upper GI endoscopy or conventional routine screening upper GI endoscopy. The lesion detection rate will be compared between both groups. Expert endoscopists will conduct this randomized study.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| CDSS-assisted screening endoscopy | Experimental | Participants will be tested for CDSS-assisted screening upper gastrointestinal endoscopy |
|
| Conventional routine screening endoscopy | Placebo Comparator | Participants will be tested for Conventional routine screening upper gastrointestinal endoscopy |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| CDSS-assisted upper GI endoscopy | Other | Expert endoscopists perform screening upper gastrointestinal endoscopy while looking at the monitor (CDSS system: automatic detection or lesion classification function) and listening to the sound of the monitor next to the original endoscopy monitor system. |
| Measure | Description | Time Frame |
|---|---|---|
| Gastric lesion detection rate | Gastric lesions (low or high grade dysplasia, early gastric cancer, or advanced gastric cancer) detection rate | Baseline (gold standard; ground truth is expert endoscopists' lesion detection) |
| Measure | Description | Time Frame |
|---|---|---|
| Lesion classification accuracy | Whether CDSS correctly diagnosed detected lesions or not | Baseline (gold standard; ground truth is histologic diagnosis of detected lesions) |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Chang Seok Bang, MD., PhD. | Contact | +82332405000 | csbang@hallym.ac.kr |
| Name | Affiliation | Role |
|---|---|---|
| Chang Seok Bang, MD, PhD | Hallym University | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Chuncheon Sacred Heart hospital | Recruiting | Chuncheon | Gangwon-do | 24253 | South Korea |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 36754065 | Derived | Gong EJ, Bang CS, Lee JJ, Baik GH, Lim H, Jeong JH, Choi SW, Cho J, Kim DY, Lee KB, Shin SI, Sigmund D, Moon BI, Park SC, Lee SH, Bang KB, Son DS. Deep learning-based clinical decision support system for gastric neoplasms in real-time endoscopy: development and validation study. Endoscopy. 2023 Aug;55(8):701-708. doi: 10.1055/a-2031-0691. Epub 2023 Feb 8. |
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| ID | Term |
|---|---|
| D013274 | Stomach Neoplasms |
| ID | Term |
|---|---|
| D005770 | Gastrointestinal Neoplasms |
| D004067 | Digestive System Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
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|
| no intervention | Other | Expert endoscopists perform routine screening upper gastrointestinal endoscopy without CDSS's help. |
|
| D004066 |
| Digestive System Diseases |
| D005767 | Gastrointestinal Diseases |
| D013272 | Stomach Diseases |