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CompletedNCT04399590NOISEUpdated Sep 16, 2021

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

An observational study in Artificial Intelligence, sponsored by Istituto Clinico Humanitas. Completed at 1 site in Italy. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2021-09-16.

Sponsored by Istituto Clinico Humanitas · Observational

Study type
Observational
Model
Cohort
Time perspective
Prospective
Enrollment
40
Ages
18 Years and older
Sex
All
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Study summary

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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Conditions studied

  • Artificial Intelligence
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In context

Lead sponsor

Istituto Clinico Humanitas is the lead sponsor of 275 studies on the registry; 71 are open to participants now.

Counted across the registry records on this site, refreshed daily.

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Who can participate

Ages eligible
18 Years and older
Sexes eligible
All
Accepts healthy volunteers
No
Sampling method
Non-probability sample

Study population

All consecutive patients scheduled for diagnostic colonoscopy.

Inclusion criteria

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

Exclusion criteria

Exclusion Criteria:

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

Observational model
Cohort
Time perspective
Prospective
Enrollment
40 participants (actual)
Patient registry
No

Interventions

  • OtherInterficial Intelligence

    Interficial Intelligence

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What researchers measure

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

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Study locations

1 site
  • Endoscopy Unit, Humanitas Research Hospital
    Rozzano, Milano 20089, Italy
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References and documents

Publications

  • Spadaccini M, Hassan C, Alfarone L, Da Rio L, Maselli R, Carrara S, Galtieri PA, Pellegatta G, Fugazza A, Koleth G, Emmanuel J, Anderloni A, Mori Y, Wallace MB, Sharma P, Repici A. Comparing the number and relevance of false activations between 2 artificial intelligence computer-aided detection systems: the NOISE study. Gastrointest Endosc. 2022 May;95(5):975-981.e1. doi: 10.1016/j.gie.2021.12.031. Epub 2022 Jan 4. PubMed 34995639 ↗

Individual participant data

Plan to share: No

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Updates

Tracking since Sep 25, 2026
No changes since tracking began. The registry record was last updated on Sep 16, 2021, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
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Registry details

Key details

Study ID
NCT04399590
Lead sponsor
Istituto Clinico Humanitas
Responsible party
Sponsor
First posted
May 22, 2020
Start date
Sep 1, 2020
Primary completion
Mar 31, 2021
Completion
Mar 31, 2021
Last update
Sep 16, 2021

Oversight

Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

Not currently enrolling

This study is completed, as verified in Sep 2021. You cannot join it, but the record below documents what was studied.

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