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OpenTrials
Completed

NCT Number: NCT04399590

Comparing the Number of False Activations Between Two Artificial Intelligence CADe Systems: the NOISE Study

One fourth of colorectal neoplasias are missed during screening colonoscopies-these can develop into colorectal cancer (CRC). In the last couple of years, Artificial Intelligence Deep learning systems were introduced in the endoscopic setting to allow for real-time computer-aided detection/characterization (CAD) of polyps with high- accuracy. Few CADe (detection) and CADx (diagnosis, characterization) have been therefore proposed with this purpose. Because CAD systems are based on deep learning where the computer directly learns polyp recognition from supervised data without any human-control on the final algorithm, their outcome incorporates some unpredictability in the clinical setting that must be cautiously interpreted after its application. This means that the endoscopist may be presented with FP images that he would have never been selected in the first place as suspicion areas. These FPs may hamper the efficiency of CADe-colonoscopy. Additional time may be required to discriminate between an actual FP and a possible false negative result. An excess of FPs may reduce the motivation of the endoscopist for CADe, leading to its underuse in clinical practice. Although the indications of a CADe must always be interpreted by physician, FP may result in unnecessary polypectomy with related adverse events when used without appropriate training. Yet, there is a lack of information among quantity and quality of False Positive signals provided by the systems. From a post-hoc analysis of a Randomized Clinical Trial, in which we extracted and analysed a video library of CADe-colonoscopy (GI Genius) performed in our institution Humanitas Clinical and Research Hospital IRCCS we aimed that False positives by CADe are primarily due to artefacts from the bowel wall. Despite a high frequency, FPs from this CADe system resulted in a negligible 1% increase of the total withdrawal time as most of them were immediately discarded by the endoscopists.

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Key information

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Endoscopy Unit, Humanitas Research Hospital

Rozzano, Milano, 20089, Italy

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Age over 18 years
  • Ability to provide and to give informed consent
  • Boston Bowel Preparation Score > 6 (>2 each segment)

Exclusion criteria

  • Boston Bowel Preparation Score < 6 (<2 each segment)
  • Patients who had chronic inflammatory bowel diseases (such as Chron or Ulcerative Colitis)
  • Inability to obtain written informed consent
  • Patient unwilling to participate to the study

Treatment and study plan

Interficial Intelligence

Other

Interficial Intelligence

Primary outcomes

  1. To evaluate the cause of False Positives (FPs) signals, their frequenTocy and time rate, on two different CAD systems: CADe (GI Genius, Medtronic) and CADe/CADx (CAD EYE, Fujifilm) and report a comparison among the two

    Time frame: 6 Months

Sponsors and collaborators

Lead sponsor

Istituto Clinico Humanitas

Other

Registry information

Acronym: NOISE

Important dates

Study start
2020
Primary completion
2021
Study completion
2021
First posted
May 22, 2020
Registry last updated
Sep 16, 2021

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

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This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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