Gastrointestinal DiseaseNCT04232462
A Multicentric Validation Study on the Accuracy of Artificial Intelligence Assisted System in Clinical Application of Digestive Endoscopy
An observational study of Gastrointestinal Disease and Endoscopy, run by Renmin Hospital of Wuhan University at 1 site in China.
Trial summary
This is an artificial intelligence-based optical artificial intelligence assisted system that can assist endoscopists in improving the quality of endoscopy. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Gastrointestinal Disease, Endoscopy, Artificial Intelligence
- What is being tested?
- Endoscopists refer to AI for diagnosis
- How many people
- 10,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2020to 31 Dec 2025
- 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
- Adult, Older adult
Study population: Patients who meet the admission criteria for endoscopic examination.
Who may be able to join
- male or female aged 18 or above;
- endoscopy and related examinations should be performed to further clarify the characteristics of digestive tract diseases;
- be able to read, understand and sign the informed consent;
- the researcher believes that the subject can understand the process of the clinical study, is willing and able to complete all the study procedures and follow-up visits, and cooperate with the study procedures;
Who cannot join
- have participated in other clinical trials, signed the informed consent and have been in the follow-up period of other clinical trials;
- drug or alcohol abuse or psychological disorder in the last 5 years;
- pregnant or nursing women;
- subjects with previous history of gastrointestinal surgery;
- the researcher considers that the subject is not suitable for endoscopy and related examination;
- high-risk diseases or other special conditions that the investigator considers inappropriate for the subject to participate in the clinical trial.
Trial locations and contacts
1 site in 1 country.
About this study
This is an artificial intelligence-based optical artificial intelligence assisted system that can assist endoscopists in improving the quality of endoscopy.
Read the detailed description
Endoscopic diagnosis and treatment play an important role in the discovery and treatment of gastrointestinal diseases.With the rapid increase in the number of endoscopies, the workload of endoscopists increases further.The high workload reduces the quality of endoscopy, leading to incomplete coverage and incomplete detection of lesions.With the rapid increase in the number of endoscopies, the workload of endoscopists increases further.The high workload reduces the quality of endoscopy, leading to incomplete coverage and incomplete detection of lesions.Therefore, carrying out deep learning and other artificial intelligence methods has good academic research and practical value for improving the quality of endoscopic diagnosis and treatment.The research and development, testing and functional evaluation of artificial intelligence devices need to use a large number of endoscopic images, and at the same time, the effectiveness and safety of artificial intelligence devices need to be verified in different hospitals and environments.Based on this, our research group intends to collect endoscopic image data from different hospitals for training and validation of the model.
- Conditions
- Gastrointestinal Disease, Endoscopy, Artificial Intelligence
- Being tested
- Endoscopists refer to AI for diagnosis
- Keywords
- Deep Learning
- Medical subject headings
- Gastrointestinal Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Endoscopists refer to AI for diagnosisDiagnostic test
The AI will provide a clinical diagnosis during endoscopy.
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy
Calculate the accuracy of AI's judgment on images and videos. Accuracy is :
Measured: 2020.1.12-2023.12.31
Sensitivity
Calculate the sensitivity of AI's judgment on images and videos. Sensitivity is : in the sample that is positive actually, the proportion that judges to be positive (for example, in the person that is really sick, be judged to be the proportion that is sick by the hospital), computation way is the ratio that true positive divides true positive add false negative (be positive actually, but judge is negative).
Measured: 2020.1.12-2023.12.31
Specificity
Calculate the specificity of AI's judgment on images and videos. Specificity is : in the samples that are actually negative, the proportion of those that are judged negative (for example, the proportion of those who are not actually ill, who are judged by the hospital to be not ill) is calculated as the ratio of true negative divided by true negative + false positive (actually negative, but judged positive).
Measured: 2020.1.12-2023.12.31
Positive Predictive Value (PPV)
The percentage of true positive people in positive test results indicates the probability that the positive test results belong to true cases.
Measured: 2020.1.12-2023.12.31
Negative Predictive Value (NPV)
The percentage of true negative to negative test results indicates the probability that the negative test results are non-cases.
Measured: 2020.1.12-2023.12.31
Receiver Operating Characteristic (ROC) Curve
Definition 1:The subject's operating characteristic curve is a coordinate graph composed of false positive rate as the horizontal axis and true positive rate as the vertical axis, and the curve drawn by the subject under specific stimulus conditions due to the different judgment criteria.
Definition 2:ROC curves were created by plotting the proportion of true positive cases (sensitivity) against the proportion of false positive cases (1-specificity), by varying the predictive probability threshold.
Measured: 2020.1.12-2023.12.31
Area Under the Curve (AUC)
Calculate the area under the curve of AI's receiver operating characteristic (ROC) curve.
Measured: 2020.1.12-2023.12.31
Secondary outcomes (5)
mean Average Precision (mAP)
mAP is setting a threshold for average precision and taking 1 or 0, and then taking the average of the sum of average precision divided by the number of values.
Measured: 2020.1.12-2023.12.31
Sørensen-Dice coefficient (F1 score)
The Sørensen-Dice coefficient is a statistic used to guage the similarity of two samples. The F1 score is a weighted average of model accuracy and recall.
Measured: 2020.1.12-2023.12.31
Recall Rate
The percentage of positive examples of predicted pairs in all samples of predicted pairs (including correct predicted positive examples and correct predicted negative examples).
Measured: 2020.1.12-2023.12.31
Positive Likelihood Ratio
Measured: 2020.1.12-2023.12.31
Negative Likelihood Ratio
Measured: 2020.1.12-2023.12.31
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 Jan 2020
- Study start1 Jan 2020
- Primary completion31 Dec 2025 (estimated)
- Study completion31 Dec 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Gastrointestinal DiseaseStudies 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
- Other
- Time perspective
- Prospective
- Enrolment
- 10,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Renmin Hospital of Wuhan University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Yu Honggang, MD, Study director, Wuhan University Renmin Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04232462
- Sponsor's protocol ID
- EA-19-006
- Status last verified
- Jan 2021
- First submitted
- 14 Jan 2020
- Last updated
- 8 Jan 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 8 Jan 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Multicentric Validation Study on the Accuracy of Artificial Intelligence Assisted System in Clinical Application of Digestive Endoscopy
- ClinicalTrials.gov ID
- NCT04232462
- Acronym / sponsor ID
- EA-19-006
- Conditions
- Gastrointestinal Disease, Endoscopy, Artificial Intelligence
- Keywords
- Deep Learning
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 10,000 participants (estimated)
- Lead sponsor
- Renmin Hospital of Wuhan UniversityUniversity, hospital or other
- Study start
- 1 Jan 2020 (actual)
- Primary completion
- 31 Dec 2025 (estimated)
- Study completion
- 31 Dec 2025 (estimated)
- First posted
- 18 Jan 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.
This is an artificial intelligence-based optical artificial intelligence assisted system that can assist endoscopists in improving the quality of endoscopy.
Detailed description
Endoscopic diagnosis and treatment play an important role in the discovery and treatment of gastrointestinal diseases.With the rapid increase in the number of endoscopies, the workload of endoscopists increases further.The high workload reduces the quality of endoscopy, leading to incomplete coverage and incomplete detection of lesions.With the rapid increase in the number of endoscopies, the workload of endoscopists increases further.The high workload reduces the quality of endoscopy, leading to incomplete coverage and incomplete detection of lesions.Therefore, carrying out deep learning and other artificial intelligence methods has good academic research and practical value for improving the quality of endoscopic diagnosis and treatment.The research and development, testing and functional evaluation of artificial intelligence devices need to use a large number of endoscopic images, and at the same time, the effectiveness and safety of artificial intelligence devices need to be verified in different hospitals and environments.Based on this, our research group intends to collect endoscopic image data from different hospitals for training and validation of the model.
Study design
- Observational model
- Other
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- Patients who meet the admission criteria for endoscopic examination.
Key inclusion criteria (4 listed)
- male or female aged 18 or above;
- endoscopy and related examinations should be performed to further clarify the characteristics of digestive tract diseases;
- be able to read, understand and sign the informed consent;
- the researcher believes that the subject can understand the process of the clinical study, is willing and able to complete all the study procedures and follow-up visits, and cooperate with the study procedures;
Key exclusion criteria (6 listed)
- have participated in other clinical trials, signed the informed consent and have been in the follow-up period of other clinical trials;
- drug or alcohol abuse or psychological disorder in the last 5 years;
- pregnant or nursing women;
- subjects with previous history of gastrointestinal surgery;
- the researcher considers that the subject is not suitable for endoscopy and related examination;
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 |
|---|---|---|
| Endoscopists refer to AI for diagnosisThe AI will provide a clinical diagnosis during endoscopy. | Diagnostic test | – |
Primary outcome
Accuracy
Time frame: 2020.1.12-2023.12.31
Calculate the accuracy of AI's judgment on images and videos. Accuracy is :
Sensitivity
Time frame: 2020.1.12-2023.12.31
Calculate the sensitivity of AI's judgment on images and videos. Sensitivity is : in the sample that is positive actually, the proportion that judges to be positive (for example, in the person that is really sick, be judged to be the proportion that is sick by the hospital), computation way is the ratio that true positive divides true positive add false negative (be positive actually, but judge is negative).
Specificity
Time frame: 2020.1.12-2023.12.31
Calculate the specificity of AI's judgment on images and videos. Specificity is : in the samples that are actually negative, the proportion of those that are judged negative (for example, the proportion of those who are not actually ill, who are judged by the hospital to be not ill) is calculated as the ratio of true negative divided by true negative + false positive (actually negative, but judged positive).
Positive Predictive Value (PPV)
Time frame: 2020.1.12-2023.12.31
The percentage of true positive people in positive test results indicates the probability that the positive test results belong to true cases.
Negative Predictive Value (NPV)
Time frame: 2020.1.12-2023.12.31
The percentage of true negative to negative test results indicates the probability that the negative test results are non-cases.
Receiver Operating Characteristic (ROC) Curve
Time frame: 2020.1.12-2023.12.31
Definition 1:The subject's operating characteristic curve is a coordinate graph composed of false positive rate as the horizontal axis and true positive rate as the vertical axis, and the curve drawn by the subject under specific stimulus conditions due to the different judgment criteria. Definition 2:ROC curves were created by plotting the proportion of true positive cases (sensitivity) against the proportion of false positive cases (1-specificity), by varying the predictive probability threshold.
Area Under the Curve (AUC)
Time frame: 2020.1.12-2023.12.31
Calculate the area under the curve of AI's receiver operating characteristic (ROC) curve.
5 secondary outcomes
mean Average Precision (mAP)
Time frame: 2020.1.12-2023.12.31
Sørensen-Dice coefficient (F1 score)
Time frame: 2020.1.12-2023.12.31
Recall Rate
Time frame: 2020.1.12-2023.12.31
Positive Likelihood Ratio
Time frame: 2020.1.12-2023.12.31
Negative Likelihood Ratio
Time frame: 2020.1.12-2023.12.31
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Jan 2021.
- Individual participant dataSharing planned: No
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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.
Yu Honggang, MD study director
Wuhan University Renmin Hospital
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 Gastrointestinal Disease and Endoscopy, run by Renmin Hospital of Wuhan University at 1 site 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 4 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: status not confirmed, as of the sponsor's last update on 8 Jan 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 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?
Results are not available in the source record.
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?
Accuracy (time frame: 2020.1.12-2023.12.31). The study lists 7 primary outcomes in all. 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 8 Jan 2021.