Sensitivity of the ADS in Identifying Polyps in Real-world ColonoscopyNCT03761771
Artificial Intelligence Identifying Polyps in Real-world Colonoscopy
An observational study of Sensitivity of the ADS in Identifying Polyps in Real-world Colonoscopy and Mean Number of Polyps Per Colonoscopy for Colonoscopists and Colonoscopists + ADS, run by Zhaoshen Li at 2 sites in China.
Trial summary
Recently, artificial intelligence (AI) assisted image recognition has made remarkable breakthroughs in various medical fields with the developing of deep learning and conventional neural networks (CNNs). Read more about this study
- Who is it for?
- Ages 18 to 75 years
- Conditions the study is for
- Sensitivity of the ADS in Identifying Polyps in Real-world Colonoscopy, Mean Number of Polyps Per Colonoscopy for Colonoscopists and Colonoscopists + ADS
- What is being tested?
- colonoscopy withdrawal with the ADS monitoring
- Type of treatment
- Medical device
- How many people
- 209 participants
- Where
- 2 sitesChina
- Study dates
- 1 Nov 2018to 10 Dec 2018
- 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 75 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: consecutive outpatient who recieved colonoscopy
Who may be able to join
- patients receiving screening colonoscopy
- patients receiving surveillance colonoscopy
- patients receiving diagnostic colonoscopy
Who cannot join
- patients with declined consent
- patients with poor bowel preparation
- patients with failed cecal intubation
- patients with colonic resection
- patients with inflammatory bowel diseases
- patients with polyposis
Trial locations and contacts
2 sites in 1 country.
China2 sites
Changhai Hospital, Second Military Medical University
Shanghai, 200433
Changhai Hospital
Shanghai, 200433
About this study
Official title: Validating the Performance of Artificial Intelligence in Identifying Polyps in Real-world Colonoscopy
Recently, artificial intelligence (AI) assisted image recognition has made remarkable breakthroughs in various medical fields with the developing of deep learning and conventional neural networks (CNNs). However, all current AI assisted-diagnosis systems (ADSs) were established and validated on endoscopic images or selected videos, while its actual assisted-diagnosis performance in real-world colonoscopy is up to now unknown. Therefore, we validated the performance of an ADS in real-world colonoscopy, which is based on deep learning algorithm and CNNs, trained and tested in multicenter datasets of 20 endoscopy centers.
Read the detailed description
The ADS were established in changhai digestive endoscopy center to assess its efficacy in clinical practice. The ADS automatically initiated once the ileocecal valve was pictured by the colonoscopist or the colonoscopist recorded any image of colon during the insertion. When colonoscopists withdrew the colonoscopies and inspect the colons, the video streaming of colonoscopies was real-time switched to the ADS, which made it feasible to identify and classify lesions in real time. Colonoscopists were invited to respond if they doubted potential polyps in the screen, and the ADS also made a voice when identifying potential polyps, followed by repeatedly inspecting to confirm the existence of lesions. The voice of ADS could be real-time heard by colonoscopists, while the screen of ADS was placed right behind colonoscopists, where polyps identified by ADS could be seen after the colonoscopists' turning but not simultaneously. The lesion detection by ADS or colonoscopists were determined as follow: A. polyps only identified by ADS, which was considered to be missed by colonoscopists: polyps were reported by the ADS and the colonoscopists did not know the location of polyps without reminder of the ADS until the polyps disappeared from the view; B. polyps first identified by ADS: polyps were first reported by the ADS and the colonoscopists also later knew the location of polyps by themselves; C. polyps simultaneously identified by the ADS and colonoscopists: the time of reporting polyps was closely synchronal (within 1 second); D. polyps first reported by colonoscopists: polyps were first reported by the colonoscopists and the ADS also later identified the location of polyps before the colonoscopists unfolded and pictured the polyps; E. polyps only reported by colonoscopists, which was considered to be missed by the ADS: polyps were reported by the colonoscopists and the ADS did not identify the location of polyps until colonoscopists unfolded and pictured the polyps. Besides, the false-positives of real-world ADS were also reported with potential causes analyzed by colonoscopists.
- Conditions
- Sensitivity of the ADS in Identifying Polyps in Real-world Colonoscopy, Mean Number of Polyps Per Colonoscopy for Colonoscopists and Colonoscopists + ADS
- Being tested
- colonoscopy withdrawal with the ADS monitoring
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
colonoscopy withdrawal with the ADS monitoringDevice
During the testing of trained ADS, when the system doubts colonic lesions from the input data of the test images, a rectangular frame was displayed in the endoscopic image to surround the lesion. If the system confirmed it as the colonic lesions, a sound of reminder will be played and the types of lesions (non-adenomatous polyps, adenomatous polyps and colorectal cancers) will be classified by the system. We adopted several standards to define the identification and classification of colonic lesions: 1) when the system identified and confirmed any lesion in the images of no polyps or cancers, the results were judged to be false-positive. 2) when the system both confirmed and correctly localized the lesions in images (IoU > 0.3), the results were judged to be true-positive. 3) when the system did not confirm or correctly localize the lesions, the results were judged as false-negative. 4) when system confirmed no lesions in the normal images, the results were judged to be true-negative.
| Study group | Role | What they receive |
|---|---|---|
| colonoscopy withdrawal with the ADS monitoring | The ADS automatically initiated once the ileocecal valve was pictured by the colonoscopist or the colonoscopist recorded any image of colon during the insertion. When colonoscopists withdrew the colonoscopies and inspect the colons, the video streaming of colonoscopies was real-time switched to the ADS, which made it feasible to identify and classify lesions in real time. Receives: Device: colonoscopy withdrawal with the ADS monitoring |
What is being measured
The primary outcome is the main question the study is designed to answer.
sensitivity of the ADS in identifying polyps
Polyps that were only reported by colonoscopists were considered to be missed by the ADS (polyps were reported by the colonoscopists and the ADS did not identify the location of polyps until colonoscopists unfolded and pictured the polyps.)
Measured: 1 hour
Secondary outcomes (1)
false positves of the ADS per colonoscopy withdrawal
when the system identified and confirmed any lesion in the images with no polyps or cancers appearing, the results were judged to be false-positive.
Measured: 1 hour
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Dec 2018
- Study start1 Nov 2018
- Primary completion10 Dec 2018
- Study completion10 Dec 2018
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (4)Papers the sponsor has linked to this study.
- Other trials for Sensitivity of the ADS in Identifying Polyps in Real-world ColonoscopyStudies 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
- Case only
- Time perspective
- Prospective
- Enrolment
- 209 participants (actual)
Sponsor and investigators
- Lead sponsor
- Zhaoshen Li
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Zhaoshen Li, Changhai Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03761771
- Sponsor's protocol ID
- AI-1
- Status last verified
- Dec 2018
- First submitted
- 30 Nov 2018
- Last updated
- 17 Dec 2018
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Byrne MF, Chapados N, Soudan F, Oertel C, Linares Perez M, Kelly R, Iqbal N, Chandelier F, Rex DK. Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model. Gut. 2019 Jan;68(1):94-100. doi: 10.1136/gutjnl-2017-314547. Epub 2017 Oct 24.
PubMed 29066576Wang Z, Meng Q, Wang S, Li Z, Bai Y, Wang D. Deep learning-based endoscopic image recognition for detection of early gastric cancer: a Chinese perspective. Gastrointest Endosc. 2018 Jul;88(1):198-199. doi: 10.1016/j.gie.2018.01.029. No abstract available.
PubMed 29935613Urban G, Tripathi P, Alkayali T, Mittal M, Jalali F, Karnes W, Baldi P. Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy. Gastroenterology. 2018 Oct;155(4):1069-1078.e8. doi: 10.1053/j.gastro.2018.06.037. Epub 2018 Jun 18.
PubMed 29928897Wang Z, Zhao S, Bai Y. Artificial Intelligence as a Third Eye in Lesion Detection by Endoscopy. Clin Gastroenterol Hepatol. 2018 Sep;16(9):1537. doi: 10.1016/j.cgh.2018.04.032. No abstract available.
PubMed 30119878
Study at a glance
From the registry record, last updated by the sponsor on 17 Dec 2018. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Validating the Performance of Artificial Intelligence in Identifying Polyps in Real-world Colonoscopy
- ClinicalTrials.gov ID
- NCT03761771
- Acronym / sponsor ID
- AI-1
- Conditions
- Sensitivity of the ADS in Identifying Polyps in Real-world Colonoscopy, Mean Number of Polyps Per Colonoscopy for Colonoscopists and Colonoscopists + ADS
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 209 participants (actual)
- Lead sponsor
- Zhaoshen LiUniversity, hospital or other
- Study start
- 1 Nov 2018 (actual)
- Primary completion
- 10 Dec 2018 (actual)
- Study completion
- 10 Dec 2018 (actual)
- First posted
- 3 Dec 2018 (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.
Recently, artificial intelligence (AI) assisted image recognition has made remarkable breakthroughs in various medical fields with the developing of deep learning and conventional neural networks (CNNs). However, all current AI assisted-diagnosis systems (ADSs) were established and validated on endoscopic images or selected videos, while its actual assisted-diagnosis performance in real-world colonoscopy is up to now unknown. Therefore, we validated the performance of an ADS in real-world colonoscopy, which is based on deep learning algorithm and CNNs, trained and tested in multicenter datasets of 20 endoscopy centers.
Detailed description
The ADS were established in changhai digestive endoscopy center to assess its efficacy in clinical practice. The ADS automatically initiated once the ileocecal valve was pictured by the colonoscopist or the colonoscopist recorded any image of colon during the insertion. When colonoscopists withdrew the colonoscopies and inspect the colons, the video streaming of colonoscopies was real-time switched to the ADS, which made it feasible to identify and classify lesions in real time. Colonoscopists were invited to respond if they doubted potential polyps in the screen, and the ADS also made a voice when identifying potential polyps, followed by repeatedly inspecting to confirm the existence of lesions. The voice of ADS could be real-time heard by colonoscopists, while the screen of ADS was placed right behind colonoscopists, where polyps identified by ADS could be seen after the colonoscopists' turning but not simultaneously. The lesion detection by ADS or colonoscopists were determined as follow: A. polyps only identified by ADS, which was considered to be missed by colonoscopists: polyps were reported by the ADS and the colonoscopists did not know the location of polyps without reminder of the ADS until the polyps disappeared from the view; B. polyps first identified by ADS: polyps were first reported by the ADS and the colonoscopists also later knew the location of polyps by themselves; C. polyps simultaneously identified by the ADS and colonoscopists: the time of reporting polyps was closely synchronal (within 1 second); D. polyps first reported by colonoscopists: polyps were first reported by the colonoscopists and the ADS also later identified the location of polyps before the colonoscopists unfolded and pictured the polyps; E. polyps only reported by colonoscopists, which was considered to be missed by the ADS: polyps were reported by the colonoscopists and the ADS did not identify the location of polyps until colonoscopists unfolded and pictured the polyps. Besides, the false-positives of real-world ADS were also reported with potential causes analyzed by colonoscopists.
Study design
- Observational model
- Case only
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| colonoscopy withdrawal with the ADS monitoring | – | Device: colonoscopy withdrawal with the ADS monitoringThe ADS automatically initiated once the ileocecal valve was pictured by the colonoscopist or the colonoscopist recorded any image of colon during the insertion. When colonoscopists withdrew the colonoscopies and inspect the colons, the video streaming of colonoscopies was real-time switched to the ADS, which made it feasible to identify and classify lesions in real time. |
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- consecutive outpatient who recieved colonoscopy
Key inclusion criteria (3 listed)
- patients receiving screening colonoscopy
- patients receiving surveillance colonoscopy
- patients receiving diagnostic colonoscopy
Key exclusion criteria (6 listed)
- patients with declined consent
- patients with poor bowel preparation
- patients with failed cecal intubation
- patients with colonic resection
- patients with inflammatory bowel diseases
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 |
|---|---|---|
| colonoscopy withdrawal with the ADS monitoringDuring the testing of trained ADS, when the system doubts colonic lesions from the input data of the test images, a rectangular frame was displayed in the endoscopic image to surround the lesion. If the system confirmed it as the colonic lesions, a sound of reminder will be played and the types of lesions (non-adenomatous polyps, adenomatous polyps and colorectal cancers) will be classified by the system. We adopted several standards to define the identification and classification of colonic lesions: 1) when the system identified and confirmed any lesion in the images of no polyps or cancers, the results were judged to be false-positive. 2) when the system both confirmed and correctly localized the lesions in images (IoU > 0.3), the results were judged to be true-positive. 3) when the system did not confirm or correctly localize the lesions, the results were judged as false-negative. 4) when system confirmed no lesions in the normal images, the results were judged to be true-negative. | Device | colonoscopy withdrawal with the ADS monitoring |
Primary outcome
sensitivity of the ADS in identifying polyps
Time frame: 1 hour
Polyps that were only reported by colonoscopists were considered to be missed by the ADS (polyps were reported by the colonoscopists and the ADS did not identify the location of polyps until colonoscopists unfolded and pictured the polyps.)
1 secondary outcome
false positves of the ADS per colonoscopy withdrawal
Time frame: 1 hour
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 17 Dec 2018.
- Background: PubMed 29066576Byrne MF, Chapados N, Soudan F, Oertel C, Linares Perez M, Kelly R, Iqbal N, Chandelier F, Rex DK. Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model. Gut. 2019 Jan;68(1):94-100. doi: 10.1136/gutjnl-2017-314547. Epub 2017 Oct 24.
- Background: PubMed 29935613Wang Z, Meng Q, Wang S, Li Z, Bai Y, Wang D. Deep learning-based endoscopic image recognition for detection of early gastric cancer: a Chinese perspective. Gastrointest Endosc. 2018 Jul;88(1):198-199. doi: 10.1016/j.gie.2018.01.029. No abstract available.
- Results publication: PubMed 29928897Urban G, Tripathi P, Alkayali T, Mittal M, Jalali F, Karnes W, Baldi P. Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy. Gastroenterology. 2018 Oct;155(4):1069-1078.e8. doi: 10.1053/j.gastro.2018.06.037. Epub 2018 Jun 18.
- Results publication: PubMed 30119878Wang Z, Zhao S, Bai Y. Artificial Intelligence as a Third Eye in Lesion Detection by Endoscopy. Clin Gastroenterol Hepatol. 2018 Sep;16(9):1537. doi: 10.1016/j.cgh.2018.04.032. No abstract available.
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.
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Sensitivity of the ADS in Identifying Polyps in Real-world Colonoscopy and Mean Number of Polyps Per Colonoscopy for Colonoscopists and Colonoscopists + ADS, run by Zhaoshen Li at 2 sites in China.
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 6 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 17 Dec 2018. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in China. 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?
sensitivity of the ADS in identifying polyps (time frame: 1 hour). 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 NCT03761771, which also lists 4 linked publications.
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 17 Dec 2018.