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The purpose of this clinical trial is to prove that the prediction capability of 'WAYMED endo' is superior to that of the endoscopists in classifying EGC based on the depth of invasion categories in gastro-endoscopic images.
The computer-aided detection·diagnosis software is an Artificial Intelligence (AI) software used to assist medical specialists in diagnostic decisions by automatically classifying EGC based on the depth of invasion categories in gastro-endoscopic images and displaying the results and possibilities on the User Interface (UI).
This clinical trial aims to evaluate the sensitivity and specificity of 'WAYMED endo' compared to that of endoscopists in classifying EGC based on the depth of invasion categories in gastro-endoscopic images. It is designed as a retrospective, single-center, double-arm, double-blind (endoscopist, investigational medical device applicator), controlled, and pivotal trial. Medical data collected retrospectively from subjects who underwent Esophagogastroduodenoscopy (EGD) and biopsy are screened. As a result of screening, medical data that meet all inclusion/exclusion criteria are enrolled and assigned to the trial and control groups.
In the trial group, the investigational medical device is applied to the images, while the endoscopists interpret the images in the control group. The Reference Standard Establishment Committee records the reference standard results as either "Mucosa (mucosal invasion)" or "Submucosa (submucosal invasion)", based on the depth of invasion of the lesion, and marks the detected lesion area with an oval on the image. The reference standard results are blinded, so they cannot be disclosed to the endoscopists or the investigational medical device applicator.
The primary endpoint includes the sensitivity (%) and specificity (%) of "WAYMED endo" and the endoscopists in classifying EGC based on the depth of invasion categories ("Mucosa" or "Submucosa") as confirmed by the reference standard. The secondary endpoint includes the accuracy (%) of "WAYMED endo" and the endoscopists in accurately classifying all early gastric cancer images as either "Mucosa" or "Submucosa", based on the depth of invasion categories as confirmed through pathological examination.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Trial group | The gastro-endoscopic images in this group are classified as "Mucosa" or "Submucosa" by WADYMED endo. |
| |
| Control group | The gastro-endoscopic images in this group are interpreted as "Mucosa" or "Submucosa" by the endoscopists. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| WADYMED endo | Device | Classification of the gastro-endoscopic images as "Mucosa" or "Submucosa" by WADYMED endo (Gastric cancer image, computer aided detection/diagnosis software) |
| Measure | Description | Time Frame |
|---|---|---|
| Clinical Sensitivity in classifying early gastric cancer (EGC) based on the depth of invasion (%) | The probability of being classified as "Mucosa (mucosal invasion)", based on the depth of invasion categories for early gastric cancer, among gastro-endoscopic images confirmed as "Mucosa" through the results of pathologic examination. | 3 months |
| Clinical Specificity in classifying early gastric cancer (EGC) based on the depth of invasion (%) | The probability of being classified as "Submucosa (submucosal invasion)", based on the depth of invasion categories for early gastric cancer, among gastro-endoscopic images confirmed as "Submucosa" through the results of pathologic examination. | 3 months |
| Measure | Description | Time Frame |
|---|---|---|
| Accuracy in classifying the depth of invasion categories ("Mucosa" or "Submucosa") for early gastric cancer (%) | The probability of accurately classifying all early gastric cancer images as either "Mucosa" or "Submucosa", based on the depth of invasion categories confirmed through pathological examination. | 3 months |
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Inclusion Criteria:
1. Patients aged 19 years or older who underwent EGD 2. Confirmed the presence of gastric cancer through the Electronic Medical Record (EMR), including reports of EGD or pathology
Exclusion Criteria:
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Gastro-endoscopic images of early gastric cancer patients who underwent EGD
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| Name | Affiliation | Role |
|---|---|---|
| Jie-Hyun Kim | Yonsei University Gangnam Severance Hospital | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Yonsei University Gangnam Severance Hospital | Seoul | South Korea |
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| Control group (the endoscopists) | Other | Interpretation of the gastro-endoscopic images as "Mucosa" or "Submucosa" by the endoscopists |
|
| ID | Term |
|---|---|
| D013274 | Stomach Neoplasms |
| ID | Term |
|---|---|
| D005770 | Gastrointestinal Neoplasms |
| D004067 | Digestive System Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D004066 | Digestive System Diseases |
| D005767 | Gastrointestinal Diseases |
| D013272 | Stomach Diseases |
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| ID | Term |
|---|---|
| D035061 | Control Groups |
| ID | Term |
|---|---|
| D015340 | Epidemiologic Research Design |
| D004812 | Epidemiologic Methods |
| D008919 | Investigative Techniques |
| D012107 | Research Design |
| D008722 | Methods |
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