Mediastinum DiseaseNCT05792280
Clinical Research on a Novel Deep-learning Based System in Mediastinal Endoscopic Ultrasound Scanning
A study testing AI system in people with Mediastinum Disease, run by The Third Xiangya Hospital of Central South University at 1 site in China.
Trial summary
The goal of this clinical trial is to develop and verify the auxiliary role of the artificial intelligence system in mediastinal ultrasound endoscopic scanning. The main questions it aims to answer are as follows: 1.The comparison of the image recognition accuracy between the artificial intelligence system and the… Read more about this study
- Who is it for?
- Ages 18 to 80 yearsHealthy volunteers can take part
- Conditions the study is for
- Mediastinum Disease
- What is being tested?
- AI system
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 180 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2021to 10 Apr 2025
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
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 80 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- 1. Age ≥18 years old, <80 years old 2.Patients who need endoscopic ultrasonography; 3. Agree to participate in this study and sign the informed consent form.
Who cannot join
- Subjects who meet any of the following criteria cannot be selected for this trial:
- First. The patient's physical condition does not meet the requirements of conventional endoscopic ultrasonography:
- Poor physical condition, including hemoglobin ≤8.0g/dl, severe cardiopulmonary insufficiency, etc.
- Anesthesia assessment failed
- Pregnancy or breastfeeding
- In the acute stage of chemical and corrosive injury, it is very easy to cause perforation
Show all 12Show fewer
- Recent acute coronary syndrome or clinically unstable ischemic heart attack
- Heart disease patients with right-to-left shunt, patients with severe pulmonary hypertension (pulmonary artery pressure> 90mmHg),patients with uncontrolled systemic hypertension and patients with adult respiratory distress syndrome.
- Second. Disagree to participate in this study.
- Third. There are other problems that do not meet the requirements of this research or that affect the results of the research:
- Mediastinal lesions have previously undergone surgery or radiotherapy and chemotherapy;
- Mental illness, drug addiction, inability to express themselves or other diseases that may affect follow-up.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
The Third Xiangya Hospital of Central South University
Changsha, Hunan, 410013
Xiaoyan Wang, Doctor, +8613974889301, wxy20011@163.com
Shiqin Huang, Doctor, +8618308312098, sqhuang0213@163.com
About this study
Official title: Clinical Research on Navigation and Quality Control System of Mediastinal Ultrasound Endoscopy Based on Deep Learning
The goal of this clinical trial is to develop and verify the auxiliary role of the artificial intelligence system in mediastinal ultrasound endoscopic scanning. The main questions it aims to answer are as follows: 1.The comparison of the image recognition accuracy between the artificial intelligence system and the ultrasound endoscopist; 2. Whether the artificial intelligence system can improve the integrity and efficiency of the mediastinum scanning for the ultrasound endoscopist. Participants will undergo mediastinal EUS with or without the assistance of the artificial intelligence system.
Read the detailed description
In this study, a total of 200 cases of mediastinal endoscopic ultrasound scanning videos will be collected. First of all, an artificial intelligence system based on deep learning for the navigation and quality control of mediastinal endoscopic ultrasonography will be established. Secondly, the artificial intelligence system will be used to identify the site and anatomical structure of the mediastinal ultrasound endoscope, and the results of the artificial intelligence system's station recognition will be compared with the results of the endoscopist's station recognition. Finally, the completeness of standard sites and scanning time of endoscopic-assisted and non-assisted AI systems were compared.
- Conditions
- Mediastinum Disease
- Being tested
- AI system
- Keywords
- EUS, AI, Quality Control, training
- Medical subject headings
- Mediastinal Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI systemDevice
Patients will undergo EUS examination with the assistance of AI system.
| Study group | Role | What they receive |
|---|---|---|
| AI-assisted group | Experimental | Subjects will undergo EUS examination with the assistance of AI system. Receives: Device: AI system |
| non-assisted group | No intervention | Subjects will undergo EUS examination without the assistance of AI system |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy
The number of correctly classified images divided by the total number of images.
Measured: 1 year
The completeness for standard station scanning
This was calculated as the number of stations successfully scanned divided by the total number of stations that should have been scanned.
Measured: Until the end of the study
Secondary outcomes (5)
Cohen's kappa coefficient
This data is to evaluate the agreement between the model and the endoscopists.
Measured: 1 year
The completeness for standard stations and anatomical landmarks per individual
The completeness of stations and anatomic landmarks of endoscopic ultrasonography by different endoscopists in the AI system assisted group and the control group were compared.
Measured: Until the end of the study
The completeness of anatomical landmarks
It calculated as the number of anatomical structures successfully scanned divided by the total number of structures that should have been scanned
Measured: Until the end of the study
Operation time
In addition to puncture, elastography, and ultrasound enhancement to observe the lesion or treatment, it can also be used to observe the time of the mediastinum.
Measured: Until the end of the study
The incidence of adverse events
The incidence of adverse events in both groups during the entire research process.
Measured: Until the end of the study
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted31 Mar 2023
- Study start1 Jan 2021
- Primary completion30 Mar 2025 (estimated)
- Study completion10 Apr 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Mediastinum DiseaseStudies of the same condition, open and under way.
- More trials run by The Third Xiangya Hospital of Central South 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind
- Who is masked
- Participant, Investigator
- Enrolment
- 180 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Xiaoyan Wang, Doctor, Principal investigator, The Third Xiangya Hospital of Central South University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05792280
- Sponsor's protocol ID
- 2023-EUS-AI-002
- Status last verified
- Nov 2024
- First submitted
- 17 Mar 2023
- Last updated
- 13 Mar 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Some data underlying this trial cannot be shared publicly due to protection of participant's privacy.
Study at a glance
From the registry record, last updated by the sponsor on 13 Mar 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Clinical Research on Navigation and Quality Control System of Mediastinal Ultrasound Endoscopy Based on Deep Learning
- ClinicalTrials.gov ID
- NCT05792280
- Acronym / sponsor ID
- 2023-EUS-AI-002
- Conditions
- Mediastinum Disease
- Keywords
- EUS, AI, Quality Control, training
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 180 participants (estimated)
- Lead sponsor
- The Third Xiangya Hospital of Central South UniversityUniversity, hospital or other
- Study start
- 1 Jan 2021 (actual)
- Primary completion
- 30 Mar 2025 (estimated)
- Study completion
- 10 Apr 2025 (estimated)
- First posted
- 31 Mar 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.
The goal of this clinical trial is to develop and verify the auxiliary role of the artificial intelligence system in mediastinal ultrasound endoscopic scanning. The main questions it aims to answer are as follows: 1.The comparison of the image recognition accuracy between the artificial intelligence system and the ultrasound endoscopist; 2. Whether the artificial intelligence system can improve the integrity and efficiency of the mediastinum scanning for the ultrasound endoscopist. Participants will undergo mediastinal EUS with or without the assistance of the artificial intelligence system.
Detailed description
In this study, a total of 200 cases of mediastinal endoscopic ultrasound scanning videos will be collected. First of all, an artificial intelligence system based on deep learning for the navigation and quality control of mediastinal endoscopic ultrasonography will be established. Secondly, the artificial intelligence system will be used to identify the site and anatomical structure of the mediastinal ultrasound endoscope, and the results of the artificial intelligence system's station recognition will be compared with the results of the endoscopist's station recognition. Finally, the completeness of standard sites and scanning time of endoscopic-assisted and non-assisted AI systems were compared.
Study design
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind (participant, investigator)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| AI-assisted group | Experimental | Device: AI systemSubjects will undergo EUS examination with the assistance of AI system. |
| non-assisted group | No intervention | –Subjects will undergo EUS examination without the assistance of AI system |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- 1. Age ≥18 years old, <80 years old 2.Patients who need endoscopic ultrasonography; 3. Agree to participate in this study and sign the informed consent form.
Key exclusion criteria (12 listed)
- Subjects who meet any of the following criteria cannot be selected for this trial:
- First. The patient's physical condition does not meet the requirements of conventional endoscopic ultrasonography:
- Poor physical condition, including hemoglobin ≤8.0g/dl, severe cardiopulmonary insufficiency, etc.
- Anesthesia assessment failed
- Pregnancy or breastfeeding
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 |
|---|---|---|
| AI systemPatients will undergo EUS examination with the assistance of AI system. | Device | AI-assisted group |
Primary outcome
Accuracy
Time frame: 1 year
The number of correctly classified images divided by the total number of images.
The completeness for standard station scanning
Time frame: Until the end of the study
This was calculated as the number of stations successfully scanned divided by the total number of stations that should have been scanned.
5 secondary outcomes
Cohen's kappa coefficient
Time frame: 1 year
The completeness for standard stations and anatomical landmarks per individual
Time frame: Until the end of the study
The completeness of anatomical landmarks
Time frame: Until the end of the study
Operation time
Time frame: Until the end of the study
The incidence of adverse events
Time frame: Until the end of the study
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 Mar 2025.
- Individual participant dataSharing planned: No. Some data underlying this trial cannot be shared publicly due to protection of participant's privacy.
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.
Xiaoyan Wang, Doctor principal investigator
The Third Xiangya Hospital of Central South University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI system in people with Mediastinum Disease, run by The Third Xiangya Hospital of Central South 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 1 inclusion and 12 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: looking for participants now, as of the sponsor's last update on 13 Mar 2025. 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Xiaoyan Wang, Doctor (+8613974889301, wxy20011@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy (time frame: 1 year). The study lists 2 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 13 Mar 2025; AzTrials last refreshed its copy on 1 Nov 2026.