Endoscopic UltrasonographyNCT05858827
To Evaluate the Capability of an EUS Automatic Image Reporting System
An observational study of Endoscopic Ultrasonography and Artificial Intelligence, run by Renmin Hospital of Wuhan University at 1 site in China.
Trial summary
In this study, the EUS intelligent picture reporting system can automatically generate reports after reading videos of EUS examinations. This function can standardize the quality of endoscopic ultrasound image reporting and reduce the work burden of ultrasound endoscopists. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Endoscopic Ultrasonography, Artificial Intelligence
- How many people
- 114 participants
- Where
- 1 siteChina
- Study dates
- 10 May 2023to 20 Dec 2023
- 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
- No healthy volunteers
- Adult, Older adult
Study population: The study population was patients undergoing endoscopic ultrasonography who met all inclusion criteria and did not meet all exclusion criteria.
Who may be able to join
- patients aged 18 years or older;
- patients with indications for endoscopic ultrasonography of the biliary pancreatic system and undergoing sedated EUS procedures;
- ability to read, understand, and sign informed consent;
Who cannot join
- patients with absolute contraindications to EUS examination;
- history of previous gastric surgery;
- pregnancy;
- severe medical illness;
- previous medical history of allergic reaction to anesthetics;
- stricture or obstruction of the esophagus;
- anatomical abnormalities of the upper gastrointestinal tract due to advanced neoplasia.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: To Evaluate the Capability of an Endoscopic Ultrasonography Automatic Image Reporting System
In this study, the EUS intelligent picture reporting system can automatically generate reports after reading videos of EUS examinations. This function can standardize the quality of endoscopic ultrasound image reporting and reduce the work burden of ultrasound endoscopists.
Read the detailed description
A well-written report is the most important way of communication between clinicians, referring doctors and patients. Reports play a key role for quality improvement in digestive endoscopy, too. Unlike digestive endoscopy, the quality of reporting in endoscopic ultrasound (EUS) has not been thoroughly evaluated and a reference standard is lacking. According to the guidance statements regarding standard EUS reporting elements developed and reviewed at the Forum for Canadian Endoscopic Ultrasound 2019 Annual Meeting, appropriate photo documentation of all relevant lesions and anatomical landmarks should be included in EUS reports and stored for future reference. Systematic photo documentation in EUS is an indicator of procedure quality according to the ASGE. Systematic photo documentation can facilitate surveillance EUS evaluations. According to an international online survey, most endosonographers used a structured tree in the report describing either normal and abnormal findings (81%) or only abnormal findings (7%). Therefore, it is necessary to develop a standardized endoscopic ultrasound image report system.
The past decades have witnessed the remarkable progress of artificial intelligence (AI) in the medical field. Deep learning, a subset of AI, has shown great potential in elaborating image analysis. In the field of digestive endoscopy, deep learning has been widely studied, including identifying focal lesions, differentiating malignant and non-malignant lesions, and so on. However, rare study works on automatic photo documentation during endoscopic ultrasound.
Our previous work has successfully developed a deep learning EUS navigation system that can identify the standard stations of the pancreas and CBD in real time. In the present study, we further constructed an EUS automatic image reporting system (EUS-AIRS). The EUS-AIRS can automatically capture images of standard stations, lesions, and biopsy procedures, and label Types of lesions, thereby generating an image report with high completeness and quality during endoscopic ultrasonography.
We tested the performance of the EUS-AIRS by testing its performance on retrospective internal and external data, and we anticipate determining the utility of the EUS-AIRS in clinical practice by testing its performance in consecutive prospective patients.
- Keywords
- Endoscopic Ultrasonography, Artificial Intelligence
What is being measured
The primary outcome is the main question the study is designed to answer.
completeness of capturing standard stations
The number of standard stations correctly captured by EUS-AIRS is divided by the number of all standard stations in the endoscopic ultrasound procedures
Measured: 2 months
Secondary outcomes (3)
accuracy of capturing standard stations
The number of standard station images correctly captured by EUS-AIRS is divided by the number of all standard station images captured by EUS-AIRS
Measured: 2 months
completeness of capturing detected lesions
The number of correct lesions captured by EUS-AIRS was divided by the number of all lesions in the endoscopic ultrasound procedure
Measured: 2 months
completeness of capturing biopsy procedures
The number of correct biopsy procedures captured by EUS-AIRS was divided by the number of all biopsy procedures in the endoscopic ultrasound procedure
Measured: 2 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted15 May 2023
- Study start10 May 2023
- Primary completion23 Oct 2023
- Study completion20 Dec 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Endoscopic UltrasonographyStudies 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
- 114 participants (actual)
Sponsor and investigators
- Lead sponsor
- Renmin Hospital of Wuhan University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Honggang Yu, Doctor, Principal investigator, Renmin Hospital of Wuhan University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05858827
- Sponsor's protocol ID
- EA-23-004
- Status last verified
- Jan 2024
- First submitted
- 5 May 2023
- Last updated
- 12 Jan 2024
- 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 12 Jan 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- To Evaluate the Capability of an Endoscopic Ultrasonography Automatic Image Reporting System
- ClinicalTrials.gov ID
- NCT05858827
- Acronym / sponsor ID
- EA-23-004
- Keywords
- Endoscopic Ultrasonography, Artificial Intelligence
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 114 participants (actual)
- Lead sponsor
- Renmin Hospital of Wuhan UniversityUniversity, hospital or other
- Study start
- 10 May 2023 (actual)
- Primary completion
- 23 Oct 2023 (actual)
- Study completion
- 20 Dec 2023 (actual)
- First posted
- 15 May 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.
In this study, the EUS intelligent picture reporting system can automatically generate reports after reading videos of EUS examinations. This function can standardize the quality of endoscopic ultrasound image reporting and reduce the work burden of ultrasound endoscopists.
Detailed description
A well-written report is the most important way of communication between clinicians, referring doctors and patients. Reports play a key role for quality improvement in digestive endoscopy, too. Unlike digestive endoscopy, the quality of reporting in endoscopic ultrasound (EUS) has not been thoroughly evaluated and a reference standard is lacking. According to the guidance statements regarding standard EUS reporting elements developed and reviewed at the Forum for Canadian Endoscopic Ultrasound 2019 Annual Meeting, appropriate photo documentation of all relevant lesions and anatomical landmarks should be included in EUS reports and stored for future reference. Systematic photo documentation in EUS is an indicator of procedure quality according to the ASGE. Systematic photo documentation can facilitate surveillance EUS evaluations. According to an international online survey, most endosonographers used a structured tree in the report describing either normal and abnormal findings (81%) or only abnormal findings (7%). Therefore, it is necessary to develop a standardized endoscopic ultrasound image report system.
The past decades have witnessed the remarkable progress of artificial intelligence (AI) in the medical field. Deep learning, a subset of AI, has shown great potential in elaborating image analysis. In the field of digestive endoscopy, deep learning has been widely studied, including identifying focal lesions, differentiating malignant and non-malignant lesions, and so on. However, rare study works on automatic photo documentation during endoscopic ultrasound.
Our previous work has successfully developed a deep learning EUS navigation system that can identify the standard stations of the pancreas and CBD in real time. In the present study, we further constructed an EUS automatic image reporting system (EUS-AIRS). The EUS-AIRS can automatically capture images of standard stations, lesions, and biopsy procedures, and label Types of lesions, thereby generating an image report with high completeness and quality during endoscopic ultrasonography.
We tested the performance of the EUS-AIRS by testing its performance on retrospective internal and external data, and we anticipate determining the utility of the EUS-AIRS in clinical practice by testing its performance in consecutive prospective patients.
Study design
- Observational model
- Other
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study population was patients undergoing endoscopic ultrasonography who met all inclusion criteria and did not meet all exclusion criteria.
Key inclusion criteria (3 listed)
- patients aged 18 years or older;
- patients with indications for endoscopic ultrasonography of the biliary pancreatic system and undergoing sedated EUS procedures;
- ability to read, understand, and sign informed consent;
Key exclusion criteria (7 listed)
- patients with absolute contraindications to EUS examination;
- history of previous gastric surgery;
- pregnancy;
- severe medical illness;
- previous medical history of allergic reaction to anesthetics;
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
completeness of capturing standard stations
Time frame: 2 months
The number of standard stations correctly captured by EUS-AIRS is divided by the number of all standard stations in the endoscopic ultrasound procedures
3 secondary outcomes
accuracy of capturing standard stations
Time frame: 2 months
completeness of capturing detected lesions
Time frame: 2 months
completeness of capturing biopsy procedures
Time frame: 2 months
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 12 Jan 2024.
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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.
Honggang Yu, Doctor principal investigator
Renmin Hospital of Wuhan University
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 Endoscopic Ultrasonography and Artificial Intelligence, 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 3 inclusion and 7 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 12 Jan 2024. 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?
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?
completeness of capturing standard stations (time frame: 2 months). 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 12 Jan 2024.