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Related Experiment Video

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Evaluation of Colorectal Cancer Risk and Prevalence by Stool DNA Integrity Detection
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Artificial Intelligence Allows Leaving-In-Situ Colorectal Polyps.

Cesare Hassan1, Giuseppina Balsamo2, Roberto Lorenzetti3

  • 1Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy; Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Milan, Italy.

Clinical Gastroenterology and Hepatology : the Official Clinical Practice Journal of the American Gastroenterological Association
|July 14, 2022
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) computer-aided diagnosis (CADx) accurately identifies colorectal polyps, achieving a 97.6% negative predictive value for small rectosigmoid lesions. This AI tool supports cost-saving strategies by potentially reducing polypectomy and pathology burdens.

Keywords:
Artificial IntelligenceColonoscopyColorectal Cancer ScreeningColorectal PolypsDiminutive PolypsLeave in SituMachine LearningOptical DiagnosisPolyp CharacterizationResect and DiscardVirtual Chromoendoscopy

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Area of Science:

  • Gastroenterology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) shows promise for cost-saving in colonoscopy due to its accuracy in optical diagnosis of colorectal polyps.
  • Clinical implementation of AI requires meeting predefined performance criteria.

Purpose of the Study:

  • To evaluate an approved computer-aided diagnosis (CADx) module for differentiating colorectal polyps.
  • To determine the negative predictive value (NPV) of CADx for adenomatous histology in small rectosigmoid lesions.
  • To assess the agreement of CADx- and histology-based surveillance intervals.

Main Methods:

  • A consecutive series of colonoscopies utilized an approved CADx module for polyp diagnosis.
  • CADx output was matched against histology as the gold standard for each polyp.
  • Negative predictive value (NPV) and agreement for surveillance intervals were calculated.

Main Results:

  • The CADx module achieved 98.6% feasibility for diagnosis in 295 small rectosigmoid lesions.
  • The NPV for ≤5-mm rectosigmoid lesions was 97.6% (95% CI, 94.1%-99.1%).
  • CADx-based surveillance intervals showed 95.6%-95.9% agreement with histology-based guidelines.

Conclusions:

  • Computer-aided diagnosis (CADx) without advanced imaging surpassed benchmarks for optical diagnosis of colorectal polyps.
  • CADx can facilitate cost-saving strategies in colonoscopy by reducing polypectomy and pathology workload.