Software Analysis on Polyp Histology PredictionNCT04425941
Polyp Artificial Intelligence Study
An observational study of Software Analysis on Polyp Histology Prediction, run by Petz Aladar County Teaching Hospital.
Trial summary
Background We are developing artificial intelligence based polyp histology prediction (AIPHP) method to automatically classify Narrow Band Imaging (NBI) magnifying colonoscopy images to predict the non-neoplastic or neoplastic histology of polyps. Read more about this study
- Who is it for?
- Ages 18 to 90 years
- Conditions the study is for
- Software Analysis on Polyp Histology Prediction
- What is being tested?
- artificial intelligence diagnosis
- How many people
- 373 participants
- Where
- No sites listed yet
- Study dates
- 5 Jan 2014to 31 May 2020
- 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 to 90 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: patients with symptoms of colorectal polypoid laesions, colorectal cancer screening patients
Who may be able to join
- endoscopic diagnosis of colorectal polyp
Who cannot join
- colonoscopy result without polyps or IBD diagnosis
About this study
Official title: Artificial Intelligence Based Colorectal Polyp Histology Prediction by Using Narrow-band Imaging Magnifying Colonoscopy
Background We are developing artificial intelligence based polyp histology prediction (AIPHP) method to automatically classify Narrow Band Imaging (NBI) magnifying colonoscopy images to predict the non-neoplastic or neoplastic histology of polyps.
Aim Our aim was to analyse the accuracy of AIPHP and NICE classification based histology predictions and also to compare the results of the two methods.
Methods We examined colorectal polyps obtained from colonoscopy patients who had polypectomy or endoscopic mucosectomy. Polyps detected by white light colonoscopy were observed then by using NBI at the optical maximum magnificent (60x). The obtained and stored NBI magnifying images were analysed by NICE classification and by AIPHP method parallelly. Pathology examinations were performed blinded to the NICE and AIPHP diagnosis, as well. Our AIPHP software is based on a machine learning method. This program measures five geometrical and colour features on the endoscopic image.
- Being tested
- artificial intelligence diagnosis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
artificial intelligence diagnosisOther
artificial intelligence prediction of colorectal polyp histology
What is being measured
The primary outcome is the main question the study is designed to answer.
Software accuracy of polyp histology prediction
Artificial intelligence software diagnosis in comparison with the polyp histology
Measured: 2014-2020
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Jun 2020
- Study start5 Jan 2014
- Primary completion31 May 2020
- Study completion31 May 2020
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Software Analysis on Polyp Histology PredictionStudies 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
- 373 participants (actual)
Sponsor and investigators
- Lead sponsor
- Petz Aladar County Teaching Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04425941
- Sponsor's protocol ID
- PetzACTHospital
- Status last verified
- Jun 2020
- First submitted
- 5 Jun 2020
- Last updated
- 11 Jun 2020
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 11 Jun 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence Based Colorectal Polyp Histology Prediction by Using Narrow-band Imaging Magnifying Colonoscopy
- ClinicalTrials.gov ID
- NCT04425941
- Acronym / sponsor ID
- PetzACTHospital
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 373 participants (actual)
- Lead sponsor
- Petz Aladar County Teaching HospitalUniversity, hospital or other
- Study start
- 5 Jan 2014 (actual)
- Primary completion
- 31 May 2020 (actual)
- Study completion
- 31 May 2020 (actual)
- First posted
- 11 Jun 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.
Background We are developing artificial intelligence based polyp histology prediction (AIPHP) method to automatically classify Narrow Band Imaging (NBI) magnifying colonoscopy images to predict the non-neoplastic or neoplastic histology of polyps.
Aim Our aim was to analyse the accuracy of AIPHP and NICE classification based histology predictions and also to compare the results of the two methods.
Methods We examined colorectal polyps obtained from colonoscopy patients who had polypectomy or endoscopic mucosectomy. Polyps detected by white light colonoscopy were observed then by using NBI at the optical maximum magnificent (60x). The obtained and stored NBI magnifying images were analysed by NICE classification and by AIPHP method parallelly. Pathology examinations were performed blinded to the NICE and AIPHP diagnosis, as well. Our AIPHP software is based on a machine learning method. This program measures five geometrical and colour features on the endoscopic image.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 to 90 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- patients with symptoms of colorectal polypoid laesions, colorectal cancer screening patients
Key inclusion criteria (1 listed)
- endoscopic diagnosis of colorectal polyp
Key exclusion criteria (1 listed)
- colonoscopy result without polyps or IBD diagnosis
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 |
|---|---|---|
| artificial intelligence diagnosisartificial intelligence prediction of colorectal polyp histology | Other | – |
Primary outcome
Software accuracy of polyp histology prediction
Time frame: 2014-2020
Artificial intelligence software diagnosis in comparison with the polyp histology
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 11 Jun 2020.
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.
Questions about this trial
What is this clinical trial studying?
An observational study of Software Analysis on Polyp Histology Prediction, run by Petz Aladar County Teaching Hospital.
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 1 inclusion and 1 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 11 Jun 2020. Status can change between updates, so confirm with the study contact before making plans.
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?
Software accuracy of polyp histology prediction (time frame: 2014-2020). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 11 Jun 2020; AzTrials last refreshed its copy on 27 Oct 2026.