Endoscopic UltrasoundNCT05421520
Effectiveness of an Ai-based Endoscopic Ultrasound Navigation System in the Training of Endoscopic Ultrasonics
A study testing Artificial intelligence assisted system in people with Endoscopic Ultrasound, run by Renmin Hospital of Wuhan University at 2 sites in China.
Trial summary
In the early stage, the investigators successfully constructed an artificial intelligence model-based ultrasonic endoscopy-assisted film reading system and named the modified system biliopancreatic Master. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Endoscopic Ultrasound
- What is being tested?
- Artificial intelligence assisted system
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 12 participants
- Where
- 2 sitesChina
- Study dates
- 8 Oct 2023to 31 May 2024
- Phase
- Interventional
- 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
Who may be able to join
- Endoscopists with experience in performing more than 300 gastroscopies; Lack of experience in independently operating an endoscopic ultrasound (EUS); No experience in EUS-guided fine-needle aspiration biopsy (FNA).
- Patient:
- Patient age ≥18 years; Patients who consecutively undergo sedated EUS procedures; Ability to read, understand, and sign the informed consent; Patients suspected of having biliary (both pancreatic and biliary) lesions based on clinical symptoms and/or radiological findings and/or laboratory test results; High-risk patients for pancreatic cancer: known genetic mutations associated with the risk of pancreatic cancer (BRCA2, BRCA1, PALB2, ATM, CDKNA/p16); familial pancreatic ductal adenocarcinoma with no known lineage mutations; Peutz-Jeghers syndrome (STK11); Lynch syndrome (MLH1/MSH2/MSH6, EPCAM, PMS2); familial adenomatous polyposis (APC) etc. Based on the preoperative cholangiopancreatography report, patients are classified into those with or without radiological findings. Patients with radiological findings are categorized by lesion location, such as the pancreas, PD, CBD, and other lesions (like gallbladder, duodenal papilla suspected of biliary invasion).
Who cannot join
- Patients with absolute contraindications to EUS examination; Previous gastric surgery; Pregnancy; Severe internal medical diseases; History of allergy to anesthesia drugs; Esophageal narrowing or obstruction; Upper gastrointestinal anatomical abnormalities caused by advanced tumors.
Trial locations and contacts
2 sites in 1 country.
China2 sites
The Eighth Affiliated Hospital, Sun Yat-sen University
Shenzhen, Guangdong, 518066
Honglei Chen, Doctor, chenhlei3@mail.sysu.edu.cn
Renmin Hospital of Wuhan University
Wuhan, Hubei, 430060
About this study
Official title: Feasibility and Effectiveness of an Ai-based Endoscopic Ultrasound Navigation System in the Training of Endoscopic ultrasonics-a Prospective, Randomized, Multi-center Clinical Trial
In the early stage, the investigators successfully constructed an artificial intelligence model-based ultrasonic endoscopy-assisted film reading system and named the modified system biliopancreatic Master. The system can realize real-time ultrasonic station recognition and anatomical mark recognition and provide doctors with corresponding operation techniques. This study aimed to verify the feasibility and effectiveness of the biliopancreatic master system developed by our project team in shortening the training period of ultrasound endoscopists through a single-center clinical study.
Read the detailed description
Endoscopic ultrasonography is essential for diagnosing and treating biliary and pancreatic diseases. The total incidence of biliary and pancreatic diseases is as high as 121/100,000, causing about 14.3/100,000 deaths worldwide every year, posing a severe threat to the health and safety of all humanity. The biliopancreatic system is located deep in the abdominal cavity, which is relatively difficult to examine and lacks specific symptoms at the early stage of the disease. Hence, the prognosis is poor, and the five-year survival rate is low. Biliopancreatic ultrasound endoscopy can obtain the cross cross-section of the biliopancreatic system through ultrasonic detection, collect tissue samples for pathological biopsy through the puncture, and carry out operations such as flushing and drainage, radiofrequency ablation, abdominal ganglion block, and gastrojejunostomy. The device integrates detection and treatment. In terms of detection, endoscopic ultrasonography is close to the lesion tissue and can avoid influencing the abdominal cavity wall, gastrointestinal gas, and other organs. Compared with X-ray, CT, MRI, and different in vitro detection methods, endoscopic ultrasonography has technical advantages of high resolution, no ionizing radiation, and solid real-time performance. In terms of treatment, endoscopic ultrasound-guided puncture through natural lumen has the characteristics of minimally invasive, lower surgical risk and cost compared with surgical treatment, which can significantly improve the prognosis of patients with biliary and pancreatic diseases. Biliopancreatic endoscopic ultrasonography is essential for diagnosing and treating biliopancreatic system diseases.
The basis of endoscopic ultrasound diagnosis and treatment lies in the accurate identification and localization of lesions by ultrasound images. Ultrasound images are cross-section images of human tissues, mainly containing texture information that is difficult to be recognized by naked human eyes; for endoscopy, doctors lacking professional training and long-term practice, difficult to accurately identify the anatomical signs in the images, which significantly affects the accuracy of identification and localization of biliopancreatic lesions. Due to the low image recognition of ultrasonic endoscopy, the operation of ultrasonic endoscopy is difficult, and the training cycle is very long. Conventional European and American endoscopic ultrasound training programs lasted for three years, but in a clinical study, two-thirds of the physicians who participated in the standard training program failed to pass the ability assessment of endoscopic ultrasound. Although ultrasound endoscopy is one of the most sensitive methods for the diagnosis of pancreatic cancer, sensitivity (87.8%) is still significantly inadequate compared with optical endoscopy for the diagnosis of gastric cancer (96%) and colon cancer (97.1%). The existence of this problem means that the existing training in ultrasonic endoscopy needs to be optimized. In recent years, Artificial intelligence (AI) machine vision technology has developed rapidly and has been widely used in transportation, finance, medicine and other fields. Several studies have shown that the diagnostic ability of AI has surpassed that of human experts in some diseases, and some studies have confirmed the feasibility of the application of AI in endoscopic ultrasound image recognition. In a previous study, an artificial intelligence model was used to assist ultrasonic endoscopists in ultrasound film reading, which could significantly improve the accuracy of physicians in image positioning and anatomical marker segmentation. In the early stage, the investigators successfully constructed an artificial intelligence model-based ultrasonic endoscopy-assisted film reading system and named the modified system biliopancreatic Master. The system can realize real-time ultrasonic station recognition and anatomical mark recognition and provide doctors with corresponding operation techniques.
- Conditions
- Endoscopic Ultrasound
- Being tested
- Artificial intelligence assisted system
- Keywords
- Ultrasound Endoscopy, Artificial intelligence, Learning curve
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial intelligence assisted systemDevice
A deep learning-based bile duct scanning system that can prompt endoscopists to scan standard stations, identify bile ducts and stones in real time
| Study group | Role | What they receive |
|---|---|---|
| A novice with ai-assisted assistance | Experimental | When patients are randomly assigned to the experimental group, the visiting physician will perform endoscopic ultrasound with the assistance of the ai-assisted system Receives: Device: Artificial intelligence assisted system |
| A novice without ai-assisted assistance | No intervention | When the patients were randomly assigned to the control group, the visiting physician would not perform endoscopic ultrasonography with the assistance of the AI-assisted system |
What is being measured
The primary outcome is the main question the study is designed to answer.
Learning cycle for ultrasound endoscopists
The number of guided examinations required by the trainees to achieve the level of stable independent operation of ultrasonic endoscopy
Measured: No more than 1 year
Secondary outcomes (1)
Duration of operation by ultrasound endoscopists
The time required to complete each operation, from the beginning to the end of the operation
Measured: No more than 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Jun 2022
- Study start8 Oct 2023 (estimated)
- Primary completion31 May 2024 (estimated)
- Study completion31 May 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Endoscopic UltrasoundStudies 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- Experimental: novices with AI-assisted system Control：without AI-assisted system
- Masking
- None (open label)
- Enrolment
- 12 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Renmin Hospital of Wuhan University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Yu Honggang, Doctor, Principal investigator, Renmin Hospital of Wuhan University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05421520
- Sponsor's protocol ID
- EA-22-005
- Status last verified
- May 2023
- First submitted
- 21 May 2022
- Last updated
- 24 Aug 2023
- Results posted
- 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 24 Aug 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Feasibility and Effectiveness of an Ai-based Endoscopic Ultrasound Navigation System in the Training of Endoscopic ultrasonics-a Prospective, Randomized, Multi-center Clinical Trial
- ClinicalTrials.gov ID
- NCT05421520
- Acronym / sponsor ID
- EA-22-005
- Conditions
- Endoscopic Ultrasound
- Keywords
- Ultrasound Endoscopy, Artificial intelligence, Learning curve
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 12 participants (estimated)
- Lead sponsor
- Renmin Hospital of Wuhan UniversityUniversity, hospital or other
- Study start
- 8 Oct 2023 (estimated)
- Primary completion
- 31 May 2024 (estimated)
- Study completion
- 31 May 2024 (estimated)
- First posted
- 16 Jun 2022 (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 the early stage, the investigators successfully constructed an artificial intelligence model-based ultrasonic endoscopy-assisted film reading system and named the modified system biliopancreatic Master. The system can realize real-time ultrasonic station recognition and anatomical mark recognition and provide doctors with corresponding operation techniques. This study aimed to verify the feasibility and effectiveness of the biliopancreatic master system developed by our project team in shortening the training period of ultrasound endoscopists through a single-center clinical study.
Detailed description
Endoscopic ultrasonography is essential for diagnosing and treating biliary and pancreatic diseases. The total incidence of biliary and pancreatic diseases is as high as 121/100,000, causing about 14.3/100,000 deaths worldwide every year, posing a severe threat to the health and safety of all humanity. The biliopancreatic system is located deep in the abdominal cavity, which is relatively difficult to examine and lacks specific symptoms at the early stage of the disease. Hence, the prognosis is poor, and the five-year survival rate is low. Biliopancreatic ultrasound endoscopy can obtain the cross cross-section of the biliopancreatic system through ultrasonic detection, collect tissue samples for pathological biopsy through the puncture, and carry out operations such as flushing and drainage, radiofrequency ablation, abdominal ganglion block, and gastrojejunostomy. The device integrates detection and treatment. In terms of detection, endoscopic ultrasonography is close to the lesion tissue and can avoid influencing the abdominal cavity wall, gastrointestinal gas, and other organs. Compared with X-ray, CT, MRI, and different in vitro detection methods, endoscopic ultrasonography has technical advantages of high resolution, no ionizing radiation, and solid real-time performance. In terms of treatment, endoscopic ultrasound-guided puncture through natural lumen has the characteristics of minimally invasive, lower surgical risk and cost compared with surgical treatment, which can significantly improve the prognosis of patients with biliary and pancreatic diseases. Biliopancreatic endoscopic ultrasonography is essential for diagnosing and treating biliopancreatic system diseases.
The basis of endoscopic ultrasound diagnosis and treatment lies in the accurate identification and localization of lesions by ultrasound images. Ultrasound images are cross-section images of human tissues, mainly containing texture information that is difficult to be recognized by naked human eyes; for endoscopy, doctors lacking professional training and long-term practice, difficult to accurately identify the anatomical signs in the images, which significantly affects the accuracy of identification and localization of biliopancreatic lesions. Due to the low image recognition of ultrasonic endoscopy, the operation of ultrasonic endoscopy is difficult, and the training cycle is very long. Conventional European and American endoscopic ultrasound training programs lasted for three years, but in a clinical study, two-thirds of the physicians who participated in the standard training program failed to pass the ability assessment of endoscopic ultrasound. Although ultrasound endoscopy is one of the most sensitive methods for the diagnosis of pancreatic cancer, sensitivity (87.8%) is still significantly inadequate compared with optical endoscopy for the diagnosis of gastric cancer (96%) and colon cancer (97.1%). The existence of this problem means that the existing training in ultrasonic endoscopy needs to be optimized. In recent years, Artificial intelligence (AI) machine vision technology has developed rapidly and has been widely used in transportation, finance, medicine and other fields. Several studies have shown that the diagnostic ability of AI has surpassed that of human experts in some diseases, and some studies have confirmed the feasibility of the application of AI in endoscopic ultrasound image recognition. In a previous study, an artificial intelligence model was used to assist ultrasonic endoscopists in ultrasound film reading, which could significantly improve the accuracy of physicians in image positioning and anatomical marker segmentation. In the early stage, the investigators successfully constructed an artificial intelligence model-based ultrasonic endoscopy-assisted film reading system and named the modified system biliopancreatic Master. The system can realize real-time ultrasonic station recognition and anatomical mark recognition and provide doctors with corresponding operation techniques.
Study design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- Experimental: novices with AI-assisted system Control：without AI-assisted system
| Arm | Type | Receives |
|---|---|---|
| A novice with ai-assisted assistance | Experimental | Device: Artificial intelligence assisted systemWhen patients are randomly assigned to the experimental group, the visiting physician will perform endoscopic ultrasound with the assistance of the ai-assisted system |
| A novice without ai-assisted assistance | No intervention | –When the patients were randomly assigned to the control group, the visiting physician would not perform endoscopic ultrasonography with the assistance of the AI-assisted system |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- Endoscopists with experience in performing more than 300 gastroscopies; Lack of experience in independently operating an endoscopic ultrasound (EUS); No experience in EUS-guided fine-needle aspiration biopsy (FNA).
- Patient age ≥18 years; Patients who consecutively undergo sedated EUS procedures; Ability to read, understand, and sign the informed consent; Patients suspected of having biliary (both pancreatic and biliary) lesions based on clinical symptoms and/or radiological findings and/or laboratory test results; High-risk patients for pancreatic cancer: known genetic mutations associated with the risk of pancreatic cancer (BRCA2, BRCA1, PALB2, ATM, CDKNA/p16); familial pancreatic ductal adenocarcinoma with no known lineage mutations; Peutz-Jeghers syndrome (STK11); Lynch syndrome (MLH1/MSH2/MSH6, EPCAM, PMS2); familial adenomatous polyposis (APC) etc. Based on the preoperative cholangiopancreatography report, patients are classified into those with or without radiological findings. Patients with radiological findings are categorized by lesion location, such as the pancreas, PD, CBD, and other lesions (like gallbladder, duodenal papilla suspected of biliary invasion).
Key exclusion criteria (1 listed)
- Patients with absolute contraindications to EUS examination; Previous gastric surgery; Pregnancy; Severe internal medical diseases; History of allergy to anesthesia drugs; Esophageal narrowing or obstruction; Upper gastrointestinal anatomical abnormalities caused by advanced tumors.
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 |
|---|---|---|
| Artificial intelligence assisted systemA deep learning-based bile duct scanning system that can prompt endoscopists to scan standard stations, identify bile ducts and stones in real time | Device | A novice with ai-assisted assistance |
Primary outcome
Learning cycle for ultrasound endoscopists
Time frame: No more than 1 year
The number of guided examinations required by the trainees to achieve the level of stable independent operation of ultrasonic endoscopy
1 secondary outcome
Duration of operation by ultrasound endoscopists
Time frame: No more than 1 year
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 Aug 2023.
- 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, Doctor principal investigator
Renmin Hospital of Wuhan University
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Artificial intelligence assisted system in people with Endoscopic Ultrasound, run by Renmin Hospital of Wuhan University 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 1 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 24 Aug 2023. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Yu Honggang, Doctor (13871281899, yuhonggang@whu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Learning cycle for ultrasound endoscopists (time frame: No more than 1 year). 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 24 Aug 2023.