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Real-Time Characterization of Colonic Polyps: A Multicenter Prospective Study Evaluating the CAD-EYE System in

Marie Maigné1, Maxime Saunier1, Valentine Renaudeau2

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The CAD-EYE® artificial intelligence system showed insufficient sensitivity for diagnosing neoplastic colorectal polyps in real-time. However, AI significantly improved polyp detection rates during colonoscopies.

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artificial intelligencecolonic polypcolorectal cancer screeningreal-time characterization

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Area of Science:

  • Gastroenterology and Endoscopy
  • Artificial Intelligence in Medicine
  • Colorectal Cancer Screening

Background:

  • Optical diagnosis strategies like 'diagnose-and-leave' and 'resect-and-discard' aim to reduce polypectomy costs.
  • Artificial intelligence (AI) using computer-aided diagnosis (CAD) offers potential for reproducible optical diagnosis of colorectal lesions.
  • The CAD-EYE® system was evaluated for real-time characterization of colonic polyps.

Purpose of the Study:

  • To assess the performance of the CAD-EYE® system in real-time characterization of colonic polyps.
  • To compare CAD-EYE®'s accuracy against histopathology and endoscopist assessments.
  • To evaluate the impact of AI assistance on polyp detection rates.

Main Methods:

  • A cross-sectional, multicenter study involving patients undergoing screening colonoscopies at five French centers.
  • CAD-EYE® predictions were compared to endoscopist assessments and histopathology results.
  • Primary outcome: sensitivity of CAD-EYE® for neoplastic polyps (target ≥85%); secondary outcomes: specificity, PPV, NPV, endoscopist performance, and polyp detection rates.

Main Results:

  • CAD-EYE® characterization was feasible in 96% of 343 analyzed polyps.
  • Sensitivity (0.80) failed to meet the 85% threshold (p=0.064); specificity was 0.79, NPV 0.64, PPV 0.90.
  • CAD-EYE® showed higher performance for diminutive rectosigmoid polyps (DRSPs); AI-assisted colonoscopies detected more polyps (3.3 vs. 2.3, p<0.001).

Conclusions:

  • The CAD-EYE® system's performance was insufficient for accurate neoplastic colonic polyp characterization.
  • CAD-EYE® demonstrated improved performance for diminutive rectosigmoid polyps.
  • AI integration appears beneficial for enhancing polyp detection during colonoscopy.