Miss Rate of Gastric Neoplasms Under Computer-aided Endoscopy
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About
This prospective randomized trial compares AI-assisted upper gastrointestinal endoscopy with high definition upper gastrointestinal endoscopy in term of missed rate of gastric neoplasm. The investigators hypothesize the miss rate of high definition upper gastrointestinal endoscopy is higher than AI-assisted upper gastrointestinal endoscopy.
Full description
Patients will be randomly assigned to begin with AI-assisted upper gastrointestinal endoscopy follow immediately by high definition (HD) upper gastrointestinal endoscopy (AI-HD group); or start with HD upper gastrointestinal endoscopy follow immediately by AI-assisted upper gastrointestinal endoscopy (HD-AI group). The random allocation sequence is generated by a computer-generated random numerical series, with 1 representing the AI-HD group and 0 representing the HD-AI group. Randomization is conducted in blocks of four at a 1:1 ratio stratified by indications (screening/surveillance vs others), endoscopist's experience (experienced versus less experienced) and mode of sedation (unsedated vs sedated). Experienced endoscopist is defined as qualified endoscopists with more than 7 years experience in upper endoscopy, whereas less experienced endoscopists include fellows and trainees. A research assistant, not directly involved in this study, maintained all randomization codes which are contained within individual opaque envelopes. Upon obtaining patient consent, the envelope will be opened to reveal the assigned examination sequence. Patients remain blinded to their group allocation throughout the study, but the performing endoscopist is aware of the assigned allocation.
Participating endoscopists will receive training on the interpretation of real-time AI detection system as well as detection of dysplasia under HD endoscopy before performing study. All patients will fast for at least 6 hours before the procedure. All examinations will be performed with HD endoscopes (ELUXEO 7000 video system, Fujifilm Co, Tokyo, Japan) under white light. The artificial intelligence assisted gastric dysplasia localization system uses a graphical user interface for real-time display of lesion detection with bounding boxes (Fujifilm Co, Tokyo, Japan).
Each eligible patient will undergo a same-day tandem upper gastrointestinal endoscopy performed by the same endoscopist to evaluate the miss rate of gastric neoplasm. Patients first receive either AI-assisted or HD upper gastrointestinal endoscopy under white light endoscopy, immediately followed by cross-over to other procedure. Endoscopists will be assisted by a research assistant (RS), who activates or deactivates the lesion detection function of AI system between the two examinations. Both first and second examinations are conducted in accordance with the systematic gastric screening protocol, and only the gastric cavity was rescanned during the second observation. The minimal inspection time of the stomach should be 3 minute for the both examination.
Biopsies of all targeted lesions will be taken at the end of each examination. Endoscopists are instructed to biopsy lesions meeting the following criteria in HD examinations: color differences, loss of vascularity, slight elevation or depression, nodularity, thickening, abnormal convergence or flattening of folds, irregular margins, irregular discoloration, or irregular surface. During AI-assisted examinations, targeted lesions are defined as focal lesions marked in localization boxes. Endoscopists are instructed to biopsy areas stably marked with localization boxes that persisted for 5 seconds by the AI system.
Enrollment
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Inclusion criteria
- Patients aged 40 or older
- Scheduled for elective upper endoscopy
Exclusion criteria
- Pregnant women,
- Inability to provide written informed consent
- Prior gastrectomy, and
- Patients deemed unsuitable or high-risk for endoscopy with severe comorbid illnesses
Trial design
Primary purpose
Allocation
Interventional model
Masking
1,000 participants in 2 patient groups
Trial contacts and locations
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Central trial contact
Thomas Ka-Luen Lui
Data sourced from clinicaltrials.gov
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