Clinical Validation of Polydeep: an Artificial Intelligence-based Computer-aided Polyp Detection (CADe) and Characterization (CADx) System
Condition: Colorectal Cancer
MeSH terms: Colorectal Neoplasms
Study type: Interventional

Summary: 
      This study is a clinical validation of Polydeep, a computer-aided polyp detection (CADe) and
      characterization (CADx) system. Polydeep Advance 1 is an unicentric prospective diagnostic
      tests trial with a paired study design. The hypothesis of the study is that Polydeep, a CAD
      system, is more sensitive than a blinded endoscopists for the detection of colorectal polyps
      in a high definition colonoscopy.
    

Detailed description: 
      Colorectal cancer (CRC) is the most frequently cancer in western world. A fundamental tool
      for detection and prevention is the colonoscopy. The detection and endoscopic resection of
      colorectal polyps, the precursor lesion of CRC can reduce CRC incidence and mortality.
      Adenoma detection rate is the most used endoscopic quality indicator. The improvement of this
      indicator is related to the reduction of postcolonoscopy CRC incidence and mortality.

      Colorectal polyp diagnosis is based on endoscopic resection and histological analysis. An
      accurate optical diagnosis could avoid histological lesion of smaller lesions, reducing the
      costs associated with histological diagnosis. The NICE international classification has
      proposed the use of high definition endoscopes that have Narrow Band Imaging. However, NICE
      must be used by endoscopists who are sufficiently prepared and who have overcome the learning
      curve. Therefore, optical histology diagnosis with high accuracy independently of the center
      and the endoscopist is necessary.

      Computer Aid Diagnosis (CAD) systems based on Artificial Intelligence are experiencing
      exponential development in the field of medical image analysis. The development of the CAD
      system is based on the creation of large databases of endoscopic images and/or videos, on the
      training, development and validation of diagnostic algorithms in such databases and, finally,
      on prospective clinical validation in patients undergoing colonoscopy. The goal of CAD
      systems in colonoscopy is double. First, it aims to increase the detection of polyps (CADe)
      in general, and of adenomas and serrated lesions in particular. The second objective is to
      characterize (CADx) the histology of detected lesion.

      Polydeep CAD is a functional prototype. It is capable of detecting, locating and classifying
      colorectal polyps. In vivo validation data shows that Polydeep has high diagnostic accuracy
      for polyp identification and that this accuracy can be accommodated. The aim of Polydeep
      advance 1 is to perform the clinical validation within a diagnostic test trial with a paired
      study design. We will compare the sensitivity of Polydeep to endoscopists blinded to Polydeep
      in high definition colonoscopy.
    

Intervention name(s): Sensitivity of Polydeep vs high experienced endoscopists for colorectal polyp detection
Intervention type(s): Diagnostic Test
Intervention description(s): Both diagnostic interventions will be performed in all patients
High definition colonoscopy performed by high experienced endoscopists blinded to Polydeep.
Polydeep: a CADe and CADx system. The gold standard will be the histological diagnosis of the lesion.
Intervention model: Single Group Assignment
Intervention model description: A high definition colonoscopy will be performed by a high experienced endoscopists. The endoscopists will be blinded to a second monitor with the Polydeep evaluation. A second observer will annotate the lesions detected during colonoscopy withdrawal and will inform the endoscopists if Polydeep detects a lesion that is not detected by the endoscopists.
Masking: None (Open Label)
Primary purpose: Diagnostic
Keywords: Colorectal adenomas; Colorectal polyps; Serrated lesions; Computer-Aided detection System (CAD)