Skip to content

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

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

Status
Completed
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT04399590
Acronym
NOISE
Enrollment
40
Registered
2020-05-22
Start date
2020-09-01
Completion date
2021-03-31
Last updated
2021-09-16

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Artificial Intelligence

Brief 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.

Interventions

OTHERInterficial Intelligence

Interficial Intelligence

Sponsors

Istituto Clinico Humanitas
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No

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

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

Design outcomes

Primary

MeasureTime frame
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 two6 Months

Countries

Italy

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 19, 2026