Clinical trial NCT06623331: The Implementation of Computer-aided Detection in Training Improves the Quality of Future Colonoscopies
Official title: The Implementation of Computer-aided Detection in an Initial Endoscopy Training Improves the Quality Measures of Trainees' Future Colonoscopies
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Quality Indicators, Health Care
Colonoscopy Diagnostic Techniques and Procedures
Artificial Intelligence (AI)
Keywords: Quality Indicators
Colonoscopy
Artificial Intelligence (AI)
Computer-aided detection (CADe)
Adenoma detection rate (ADR)
Lead sponsor: Jagiellonian University
Sponsor class: OTHER
First posted: 2024-10-02
Enrollment: 6000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_CONTROL
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: This study included 6,000 adult patients who underwent a colonoscopy for various reasons in a single high-volume endoscopy clinic in Krakow.

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.

Eligibility criteria: Inclusion Criteria:

* adult participants who underwent a colonoscopy for various reasons performed by specific endoscopists that were assessed in terms of quality indicators

Exclusion Criteria:

* a history of bowel resection
* confirmed inflammatory bowel disease
* suspicion of polyps or cancer in other imaging tests
* suspicion of familial adenomatous polyposis

Primary outcomes:
measure: Serrated polyp detection rate (SDR)
timeFrame: During the colonoscopy examination
measure: withdrawal time
timeFrame: During the colonoscopy examination
measure: Cecal intubation rate (CIR)
timeFrame: During the colonoscopy examination
measure: Adenoma Detection Rate (ADR)
timeFrame: During the colonoscopy examination
measure: Advanced adenoma detection rate (AADR)
timeFrame: During the colonoscopy examination
measure: Adenoma per colonoscopy score (APC)
timeFrame: During the colonoscopy examination
Secondary outcomes:
measure: Laterally spreading tumor detection rate
timeFrame: During the colonoscopy examination
Other outcomes:
Locations:
city: Kraków
country: Poland
facility: Jagiellonian University
Study officials:
affiliation: Jagiellonian University
name: Zofia Orzeszko, MD
role: PRINCIPAL_INVESTIGATOR
affiliation: Jagiellonian University
name: Miroslaw Szura, PhD, Prof.
role: STUDY_CHAIR
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