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Area of Science:

  • Gastroenterology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) systems are increasingly used in gastroenterology to improve colonoscopy quality outcomes.
  • Novel AI computer-aided detection systems aim to enhance polyp detection during colonoscopy.

Purpose of the Study:

  • To assess clinical quality outcomes of white-light colonoscopy with and without the DEtection of Elusive Polyps (DEEP2) AI system.
  • To evaluate the efficacy of DEEP2 in improving adenoma detection rates (ADR) and polyp detection rates (PDR).

Main Methods:

  • A prospective, randomized (1:1), controlled study was conducted at a single endoscopy center.
  • Colonoscopies were performed using Fuji 7000 series colonoscopes with or without the DEEP2 AI system.
  • Participants aged 40-85 years were included, with specific exclusion criteria applied.

Main Results:

  • The DEEP2 system significantly increased the adenoma detection rate (ADR) from 10% to 27%-37% (P=.0057).
  • Adenomas per colonoscopy (APCs) and polyp detection rates (PDR) were significantly higher with DEEP2 (P<.001).
  • Significant improvements in ADR and APCs were observed in the right-sided colon, with a lower false alert rate compared to other systems.

Conclusions:

  • The DEtection of Elusive Polyps (DEEP2) AI system demonstrates significant improvements in key colonoscopy quality metrics.
  • Physicians and nurses unanimously expressed a desire to continue using the DEEP2 system post-study.
  • The DEEP2 system shows promise for enhancing colonoscopy effectiveness and patient care.