Stomach NeoplasmsNCT05819099
The Role of Artificial Intelligence in Endoscopic Diagnosis of Esophagogastric Junctional Adenocarcinoma：A Single Center, Case-control, Diagnostic Study
An observational study of Stomach Neoplasms, run by Qilu Hospital of Shandong University.
Trial summary
This is a single center, case-control, diagnostic study.The aim of this study is to use deep learning methods to retrospectively analyze the imaging data of gastrointestinal endoscopy in Qilu Hospital, and construct an artificial intelligence model based on endoscopic images for detecting and determining the depth of… Read more about this study
- Who is it for?
- Ages 18 to 75 yearsHealthy volunteers can take part
- Conditions the study is for
- Stomach Neoplasms
- What is being tested?
- An Intelligent Endoscopic Diagnosis System Developed and Verified Based on Deep Learning
- How many people
- 200 participants
- Where
- No sites listed yet
- Study dates
- Dec 2023to Apr 2026
- 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
- Healthy volunteers accepted
- Adult, Older adult
Study population: This study included endoscopic images of the esophageal gastric junction retrieved from the Endoscopy Center of Qilu Hospital for training and testing the model.The study population underwent pathological examination and the pathological results were used as the gold standard.
Who may be able to join
- This study included endoscopic images of patients aged 18 and above who underwent endoscopic examination or treatment
- All patients in the case group need to be pathologically confirmed as esophageal gastric junction adenocarcinoma, and a pathologist has conducted a standardized pathological evaluation of the tumor classification of the lesion, including the overall appearance, size, differentiation type, depth of infiltration, presence or absence of lymphatic/vascular invasion, surgical margin status, etc.
- The endoscopic images of the control group patients need to be confirmed by biopsy pathology or at least two experienced endoscopists (with operating experience>5000 cases) to jointly confirm that they have clear benign manifestations
Who cannot join
- The patient has a previous history of endoscopic treatment or surgery for the esophageal gastric junction.
- Necessary clinical information cannot be provided during the research process (patient age, gender, lesion characteristics, endoscopic manifestations, endoscopic images, etc.)
- Low quality endoscopic images, such as those severely affected by bleeding, aperture, blurring, defocusing, artifacts, or excessive mucus after biopsy.
Trial locations and contacts
No sites are listed yet.
About this study
This is a single center, case-control, diagnostic study.The aim of this study is to use deep learning methods to retrospectively analyze the imaging data of gastrointestinal endoscopy in Qilu Hospital, and construct an artificial intelligence model based on endoscopic images for detecting and determining the depth of invasion of esophagogastric junctional adenocarcinoma.This study will also compare the established AI model with the diagnostic results of endoscopists to evaluate the clinical auxiliary value of the model for endoscopists.The research includes stages such as data collection and preprocessing, artificial intelligence model development, model testing and evaluation. The gastroscopy image dataset constructed by this research institute mainly includes three modes of endoscopic imaging: white light endoscopy, optical enhancement endoscopy (OE), and narrowband imaging endoscopy (NBI).
- Conditions
- Stomach Neoplasms
- Being tested
- An Intelligent Endoscopic Diagnosis System Developed and Verified Based on Deep Learning
- Medical subject headings
- Stomach Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
An Intelligent Endoscopic Diagnosis System Developed and Verified Based on Deep LearningDiagnostic test
This study will compare the established AI model with the diagnostic results of endoscopists to evaluate the clinical auxiliary value of the model for endoscopists.
| Study group | Role | What they receive |
|---|---|---|
| Training Set | Not described Receives: Diagnostic Test: An Intelligent Endoscopic Diagnosis System Developed and Verified Based on Deep Learning | |
| Test Set | Not described Receives: Diagnostic Test: An Intelligent Endoscopic Diagnosis System Developed and Verified Based on Deep Learning | |
| Verification Set | Not described Receives: Diagnostic Test: An Intelligent Endoscopic Diagnosis System Developed and Verified Based on Deep Learning |
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity
The researchers calculated the sensitivity of the established AI model and compared it with endoscopists of different levels.
Measured: 36 months
Specificity
The researchers calculated the specificity of the established AI model and compared it with endoscopists of different levels.
Measured: 36 months
Negative predictive value
The researchers calculated the negative predictive value of the established AI model and compared it with endoscopists of different levels.
Measured: 36 months
Positive predictive value
The researchers calculated the positive predictive value of the established AI model and compared it with endoscopists of different levels.
Measured: 36 months
Accuracy
The researchers calculated accuracy positive predictive value of the established AI model and compared it with endoscopists of different levels.
Measured: 36 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Apr 2023
- Study startDec 2023 (estimated)
- Primary completionApr 2025 (estimated)
- Study completionApr 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Stomach NeoplasmsStudies of the same condition, open and under way.
- More trials run by Qilu Hospital of Shandong UniversityThe sponsor's other registered studies.
- 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-control
- Time perspective
- Retrospective
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Qilu Hospital of Shandong University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05819099
- Sponsor's protocol ID
- 2023SDU-QILU-1
- Status last verified
- Apr 2023
- First submitted
- 6 Apr 2023
- Last updated
- 18 Nov 2023
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 18 Nov 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Role of Artificial Intelligence in Endoscopic Diagnosis of Esophagogastric Junctional Adenocarcinoma：A Single Center, Case-control, Diagnostic Study
- ClinicalTrials.gov ID
- NCT05819099
- Acronym / sponsor ID
- 2023SDU-QILU-1
- Conditions
- Stomach Neoplasms
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Qilu Hospital of Shandong UniversityUniversity, hospital or other
- Study start
- Dec 2023 (estimated)
- Primary completion
- Apr 2025 (estimated)
- Study completion
- Apr 2026 (estimated)
- First posted
- 19 Apr 2023 (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 a single center, case-control, diagnostic study.The aim of this study is to use deep learning methods to retrospectively analyze the imaging data of gastrointestinal endoscopy in Qilu Hospital, and construct an artificial intelligence model based on endoscopic images for detecting and determining the depth of invasion of esophagogastric junctional adenocarcinoma.This study will also compare the established AI model with the diagnostic results of endoscopists to evaluate the clinical auxiliary value of the model for endoscopists.The research includes stages such as data collection and preprocessing, artificial intelligence model development, model testing and evaluation. The gastroscopy image dataset constructed by this research institute mainly includes three modes of endoscopic imaging: white light endoscopy, optical enhancement endoscopy (OE), and narrowband imaging endoscopy (NBI).
Study design
- Observational model
- Case-control
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Training Set | – | Diagnostic Test: An Intelligent Endoscopic Diagnosis System Developed and Verified Based on Deep Learning |
| Test Set | – | Diagnostic Test: An Intelligent Endoscopic Diagnosis System Developed and Verified Based on Deep Learning |
| Verification Set | – | Diagnostic Test: An Intelligent Endoscopic Diagnosis System Developed and Verified Based on Deep Learning |
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- This study included endoscopic images of the esophageal gastric junction retrieved from the Endoscopy Center of Qilu Hospital for training and testing the model.The study population underwent pathological examination and the pathological results were used as the gold standard.
Key inclusion criteria (3 listed)
- This study included endoscopic images of patients aged 18 and above who underwent endoscopic examination or treatment
- All patients in the case group need to be pathologically confirmed as esophageal gastric junction adenocarcinoma, and a pathologist has conducted a standardized pathological evaluation of the tumor classification of the lesion, including the overall appearance, size, differentiation type, depth of infiltration, presence or absence of lymphatic/vascular invasion, surgical margin status, etc.
- The endoscopic images of the control group patients need to be confirmed by biopsy pathology or at least two experienced endoscopists (with operating experience>5000 cases) to jointly confirm that they have clear benign manifestations
Key exclusion criteria (3 listed)
- The patient has a previous history of endoscopic treatment or surgery for the esophageal gastric junction.
- Necessary clinical information cannot be provided during the research process (patient age, gender, lesion characteristics, endoscopic manifestations, endoscopic images, etc.)
- Low quality endoscopic images, such as those severely affected by bleeding, aperture, blurring, defocusing, artifacts, or excessive mucus after biopsy.
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 |
|---|---|---|
| An Intelligent Endoscopic Diagnosis System Developed and Verified Based on Deep LearningThis study will compare the established AI model with the diagnostic results of endoscopists to evaluate the clinical auxiliary value of the model for endoscopists. | Diagnostic test | Test Set, Training Set, Verification Set |
Primary outcome
Sensitivity
Time frame: 36 months
The researchers calculated the sensitivity of the established AI model and compared it with endoscopists of different levels.
Specificity
Time frame: 36 months
The researchers calculated the specificity of the established AI model and compared it with endoscopists of different levels.
Negative predictive value
Time frame: 36 months
The researchers calculated the negative predictive value of the established AI model and compared it with endoscopists of different levels.
Positive predictive value
Time frame: 36 months
The researchers calculated the positive predictive value of the established AI model and compared it with endoscopists of different levels.
Accuracy
Time frame: 36 months
The researchers calculated accuracy positive predictive value of the established AI model and compared it with endoscopists of different levels.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 18 Nov 2023.
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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.
Questions about this trial
What is this clinical trial studying?
An observational study of Stomach Neoplasms, run by Qilu Hospital of Shandong University.
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 3 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 18 Nov 2023. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Miaomiao Ma, Bachelor (+8617657686098, mmiao6098@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sensitivity (time frame: 36 months). The study lists 5 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 18 Nov 2023.