Artificial IntelligenceNOISENCT04399590
Comparing the Number of False Activations Between Two Artificial Intelligence CADe Systems: the NOISE Study
An observational study of Artificial Intelligence, run by Istituto Clinico Humanitas at 1 site in Italy.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Artificial Intelligence
- What is being tested?
- Interficial Intelligence
- How many people
- 40 participants
- Where
- 1 siteItaly
- Study dates
- 1 Sep 2020to 31 Mar 2021
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: All consecutive patients scheduled for diagnostic colonoscopy.
Who may be able to join
- Age over 18 years
- Ability to provide and to give informed consent
- Boston Bowel Preparation Score > 6 (>2 each segment)
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
About this 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.
- Conditions
- Artificial Intelligence
- Being tested
- Interficial Intelligence
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Interficial IntelligenceOther
Interficial Intelligence
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 6 Months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 May 2020
- Study start1 Sep 2020
- Primary completion31 Mar 2021
- Study completion31 Mar 2021
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Artificial IntelligenceStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 40 participants (actual)
Sponsor and investigators
- Lead sponsor
- Istituto Clinico Humanitas
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04399590
- Sponsor's protocol ID
- 2598
- Acronym
- NOISE
- Status last verified
- Sep 2021
- First submitted
- 19 May 2020
- Last updated
- 16 Sep 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
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
Study at a glance
From the registry record, last updated by the sponsor on 16 Sep 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Comparing the Number of False Activations Between Two Artificial Intelligence CADe Systems: the NOISE Study
- ClinicalTrials.gov ID
- NCT04399590
- Acronym / sponsor ID
- NOISE / 2598
- Conditions
- Artificial Intelligence
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 40 participants (actual)
- Lead sponsor
- Istituto Clinico HumanitasUniversity, hospital or other
- Study start
- 1 Sep 2020 (actual)
- Primary completion
- 31 Mar 2021 (actual)
- Study completion
- 31 Mar 2021 (actual)
- First posted
- 22 May 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- All consecutive patients scheduled for diagnostic colonoscopy.
Key inclusion criteria (3 listed)
- Age over 18 years
- Ability to provide and to give informed consent
- Boston Bowel Preparation Score > 6 (>2 each segment)
Key exclusion criteria (4 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Interficial IntelligenceInterficial Intelligence | Other | – |
Primary outcome
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
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 16 Sep 2021.
- Derived from this study: PubMed 34995639Spadaccini 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.
- Individual participant dataSharing planned: No
Related trials for Artificial Intelligence
RecruitingObservational study
The French Gut-colo
Patients Aged 50-75 and Requiring Colonoscopy
RecruitingObservational study
A Prospective Study of Anxiety, Depression, and Bipolar Disorder in People
Anxiety Disorders, Depressive Disorder
Active, not recruitingObservational study
Direct-to-patient Teledermatology and Computer-assisted Diagnosis
New Patient Use of Teledermatology Mobile App
RecruitingObservational study
Opportunistic Breast Cancer Screening Using Non-Contrast Chest CT
Breast Cancer
Not yet recruitingObservational study
Discovering Determinants of Food Intake by Application of Artificial Intelligence to Complex, High-Dimensional Data
Healthy Volunteer
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Artificial Intelligence, run by Istituto Clinico Humanitas at 1 site in Italy.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 16 Sep 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Italy. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04399590, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 16 Sep 2021.