The Implementation of Computer-aided Detection in Training Improves the Quality of Future Colonoscopies
- Conditions
- Quality Indicators, Health CareColonoscopy Diagnostic Techniques and ProceduresArtificial Intelligence (AI)
- Interventions
- Other: AI-ehnanced endoscopy trainingOther: Conventional endoscopy training
- Registration Number
- NCT06623331
- Lead Sponsor
- Jagiellonian University
- Brief Summary
Computer-aided detection (CADe) based on artificial intelligence (AI) may improve colonoscopy quality. An increasing number of young endoscopists are trained in an AI environment. However its impact on trainees' future outcomes remains unclear. The study aimed to evaluate the quality indicators of endoscopists trained in an AI environment compared to those trained conventionally.
- Detailed Description
Computer-aided detection (CADe) based on artificial intelligence (AI) may improve colonoscopy quality. An increasing number of young endoscopists are trained in an AI environment. However its impact on trainees' future outcomes remains unclear. The study aimed to evaluate the quality indicators of endoscopists trained in an AI environment compared to those trained conventionally. A study included 6,000 adult patients who underwent a colonoscopy for various reasons. The study retrospectively evaluated the first 1,000 procedures performed by six endoscopists after completing training relying entirely on endoscopists' detection skills without AI enhancement. Three of those young endoscopists were trained with CADe, and three without additional assistance. Quality indicators were assessed in both groups. The morphology of detected polyps was evaluated to determine the influence of AI-enhanced training on laterally spreading tumors (LST) detection rate.
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 6000
- adult participants who underwent a colonoscopy for various reasons performed by specific endoscopists that were assessed in terms of quality indicators
- a history of bowel resection
- confirmed inflammatory bowel disease
- suspicion of polyps or cancer in other imaging tests
- suspicion of familial adenomatous polyposis
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description Group A AI-ehnanced endoscopy training Colonoscopies performed by endoscopists trained in AI-ehnanced environment. In the trial the quality indicators are measured after completing training, without AI enhancement. Group B Conventional endoscopy training Colonoscopies performed by endoscopists trained conventionally. In the trial the quality indicators are measured after completing training, without AI enhancement.
- Primary Outcome Measures
Name Time Method Serrated polyp detection rate (SDR) During the colonoscopy examination The percentage of colonoscopies when the serrated polyp was found
withdrawal time During the colonoscopy examination The time from the cecal intubation to the end of the examination
Cecal intubation rate (CIR) During the colonoscopy examination The percentage of colonoscopies with successful cecal intubations
Adenoma Detection Rate (ADR) During the colonoscopy examination The percentage of colonoscopies when the adenoma was found
Advanced adenoma detection rate (AADR) During the colonoscopy examination The percentage of colonoscopies when the advanced adenoma (\>10mm) was found
Adenoma per colonoscopy score (APC) During the colonoscopy examination The average number of adenomas detected in a single colonoscopy
- Secondary Outcome Measures
Name Time Method Laterally spreading tumor detection rate During the colonoscopy examination The percentage of colonoscopies when the laterally spreading tumor lesion was found
Trial Locations
- Locations (1)
Jagiellonian University
🇵🇱Kraków, Poland