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.
Study Type
OBSERVATIONAL
Enrollment
653
Classification of the gastro-endoscopic images as "Mucosa" or "Submucosa" by WADYMED endo (Gastric cancer image, computer aided detection/diagnosis software)
Interpretation of the gastro-endoscopic images as "Mucosa" or "Submucosa" by the endoscopists
Yonsei University Gangnam Severance Hospital
Seoul, South Korea
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.
Time frame: 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.
Time frame: 3 months
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.
Time frame: 3 months
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