Pancreatic CancerNCT06753318
Validation of Joint-AI in Diagnosing Pancreatic Solid Lesions
A study testing The assistance of the Joint-AI model and The assistance of the interpretable Joint-AI model in people with Pancreatic Cancer and Pancreatitis, run by Huazhong University of Science and Technology at 1 site in China.
Trial summary
This clinical trial aims to learn if a multimodal artificial intelligence (AI) model can enhance the diagnosis of pancreatic solid lesions. The main questions it aims to answer are: 1. Does the AI model enhance the diagnostic performance of endoscopists in diagnosing pancreatic solid lesions? 2. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Pancreatic Cancer, Pancreatitis, Pancreatic Neuroendocine Neoplasms (pNETs)
- What is being tested?
- The assistance of the Joint-AI model and The assistance of the interpretable Joint-AI model
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 716 participants
- Where
- 1 siteChina
- Study dates
- Jan 2025to Jan 2026
- 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 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Imaging examinations (MRI, CT, B-ultrasound) show a solid mass in the pancreas, which requires endoscopic ultrasound guided-fine needle aspiration/biopsy (EUS-FNA/B) to clarify the nature of the lesion in patients.
- Written consent provided
Who cannot join
- Age under 18 years old
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
Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
Wuhan, Hubei, 430030
Bin Cheng, 86-13986097542, b.cheng@tjh.tjmu.edu.cn
About this study
Official title: Validation of a Multimodal Artificial Intelligence Model in in Diagnosing Pancreatic Solid Lesions: a Prospective, Multicenter, Randomized, Controlled Trial
This clinical trial aims to learn if a multimodal artificial intelligence (AI) model can enhance the diagnosis of pancreatic solid lesions. The main questions it aims to answer are:
- Does the AI model enhance the diagnostic performance of endoscopists in diagnosing pancreatic solid lesions?
- Does the addition of interpretability analysis further improve the diagnostic performance of the assisted endoscopists? Researchers will compare the diagnostic performance of endoscopists with or without the assistance of the AI model.
Participants will:
- Their clinical data will be prospectively collected.
- They will be randomized to the AI-assist group and the conventional diagnosis group.
Read the detailed description
The investigators have previously developed a multimodal AI model (Joint-AI) based on endoscopic ultrasound images and clinical data to diagnose pancreatic solid lesions. This study aims to improve the Joint-AI model's performance with a prospectively collected dataset and validate it through a randomized controlled clinical trial.
- Conditions
- Pancreatic Cancer, Pancreatitis, Pancreatic Neuroendocine Neoplasms (pNETs), Autoimmune Pancreatitis, Solid Pseudopapillary Neoplasm of the Pancreas
- Being tested
- The assistance of the Joint-AI model, The assistance of the interpretable Joint-AI model
- Keywords
- pancreatic cancer, artificial intelligence, endoscopic ultrasound
- Medical subject headings
- Pancreatic Neoplasms, Pancreatitis, Autoimmune Pancreatitis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
The assistance of the Joint-AI modelDiagnostic test
Predictions given by the Joint-AI model will be provided to the endoscopists during their diagnosis
The assistance of the interpretable Joint-AI modelDiagnostic test
Predictions given by the Joint-AI model and the results of the interpretability analysis will be provided to the endoscopists during their diagnosis
| Study group | Role | What they receive |
|---|---|---|
| Conventional diagnosis | No intervention | Endoscopists diagnose pancreatic solid lesions according to endoscopic ultrasound images and clinical data. |
| Joint-AI assisted diagnosis | Experimental | Endoscopists diagnose pancreatic solid lesions based on endoscopic ultrasound images, clinical data, and predictions made by the Joint-AI model. Receives: Diagnostic Test: The assistance of the Joint-AI model |
| Interpretable Joint-AI assisted diagnosis | Experimental | Endoscopists diagnose pancreatic solid lesions based on endoscopic ultrasound images, clinical data, predictions given by the Joint-AI, and interpretability analysis results used to improve the transparency of the decision-making process of the Joint-AI model. Receives: Diagnostic Test: The assistance of the interpretable Joint-AI model |
What is being measured
The primary outcome is the main question the study is designed to answer.
Rate of correct diagnostic classification with assistance of the Joint-AI Model
The rate of correct diagnoses in discriminating pancreatic cancer from other non-cancer lesions, determined by comparing endoscopist diagnosis assisted by the Joint-AI model against the final histopathological diagnosis (reference standard).
Measured: Through study completion, an average of 1 year
Rate of correct diagnostic classification with assistance of the Interpretable Joint-AI Model
The rate of correct diagnoses in discriminating pancreatic cancer from other non-cancer lesions, determined by comparing endoscopist assessments assisted by the Interpretable Joint-AI model against the final histopathological diagnosis (reference standard)
Measured: Through study completion, an average of 1 year
Secondary outcomes (3)
Rate of correct diagnostic classification of the Joint-AI model and the interpretable Joint-AI model
Diagnostic accuracy of the AI models in this prospectively collected dataset.
Measured: Through study completion, an average of 1 year
Endoscopist-reported confidence score in diagnosis with AI assistance (the score is on a scale of 0%-100%, where 0 represents "not confident at all" and 100 represents "completely confident")
Endoscopist-reported confidence in diagnosis will be measured on a scale ranging from 0 to 100, where 0 represents "not confident at all" and 100 represents "completely confident." Higher scores indicate greater diagnostic confidence. The confidence scores will be assessed separately for diagnoses made using the Joint-AI model and the interpretable Joint-AI model.
Measured: Through study completion, an average of 1 year
Rate of correct diagnostic classification of endoscopists without AI assistance
Measured: Through study completion, an average of 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted31 Dec 2024
- Study startJan 2025 (estimated)
- Primary completionJan 2026 (estimated)
- Study completionJan 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Pancreatic CancerStudies of the same condition, open and under way.
- More trials run by Huazhong University of Science and TechnologyThe 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
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- 1. First, participants are randomized into three parallel groups: conventional diagnosis group, Joint-AI assistance group, and Interpretable Joint-AI assistance group. 2. For participants within the Joint-AI assistance group and Interpretable Joint-AI assistance group, their groups will be switched after a washout period.
- Masking
- Double blind
- Who is masked
- Participant, Outcomes assessor
- Enrolment
- 716 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Huazhong University of Science and Technology
- Sponsor type
- University, hospital or other
- Collaborators
- Beijing Union Hosptial, Affiliated Drum Tower Hospital of Nanjing University Medical School, Shanghai Longhua Hospital, Beijing Friendship Hospital, Qilu Hospital of Shandong University, Sir Run Run Shaw Hospital
- Responsible party
- Principal investigator, Bin Cheng, Huazhong University of Science and Technology
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06753318
- Sponsor's protocol ID
- Joint-AI 2024
- Status last verified
- Dec 2024
- First submitted
- 17 Dec 2024
- Last updated
- 31 Dec 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 31 Dec 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Validation of a Multimodal Artificial Intelligence Model in in Diagnosing Pancreatic Solid Lesions: a Prospective, Multicenter, Randomized, Controlled Trial
- ClinicalTrials.gov ID
- NCT06753318
- Acronym / sponsor ID
- Joint-AI 2024
- Conditions
- Pancreatic Cancer, Pancreatitis, Pancreatic Neuroendocine Neoplasms (pNETs), Autoimmune Pancreatitis, Solid Pseudopapillary Neoplasm of the Pancreas
- Keywords
- pancreatic cancer, artificial intelligence, endoscopic ultrasound
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 716 participants (estimated)
- Lead sponsor
- Huazhong University of Science and TechnologyUniversity, hospital or other
- Collaborators
- Beijing Union Hosptial, Affiliated Drum Tower Hospital of Nanjing University Medical School, Shanghai Longhua Hospital, Beijing Friendship Hospital, Qilu Hospital of Shandong University, Sir Run Run Shaw Hospital
- Study start
- Jan 2025 (estimated)
- Primary completion
- Jan 2026 (estimated)
- Study completion
- Jan 2026 (estimated)
- First posted
- 31 Dec 2024 (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 clinical trial aims to learn if a multimodal artificial intelligence (AI) model can enhance the diagnosis of pancreatic solid lesions. The main questions it aims to answer are:
- Does the AI model enhance the diagnostic performance of endoscopists in diagnosing pancreatic solid lesions?
- Does the addition of interpretability analysis further improve the diagnostic performance of the assisted endoscopists? Researchers will compare the diagnostic performance of endoscopists with or without the assistance of the AI model.
Participants will:
- Their clinical data will be prospectively collected.
- They will be randomized to the AI-assist group and the conventional diagnosis group.
Detailed description
The investigators have previously developed a multimodal AI model (Joint-AI) based on endoscopic ultrasound images and clinical data to diagnose pancreatic solid lesions. This study aims to improve the Joint-AI model's performance with a prospectively collected dataset and validate it through a randomized controlled clinical trial.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind (participant, outcomes assessor)
- Number of arms
- 3
- Model description
- 1. First, participants are randomized into three parallel groups: conventional diagnosis group, Joint-AI assistance group, and Interpretable Joint-AI assistance group. 2. For participants within the Joint-AI assistance group and Interpretable Joint-AI assistance group, their groups will be switched after a washout period.
| Arm | Type | Receives |
|---|---|---|
| Conventional diagnosis | No intervention | –Endoscopists diagnose pancreatic solid lesions according to endoscopic ultrasound images and clinical data. |
| Joint-AI assisted diagnosis | Experimental | Diagnostic Test: The assistance of the Joint-AI modelEndoscopists diagnose pancreatic solid lesions based on endoscopic ultrasound images, clinical data, and predictions made by the Joint-AI model. |
| Interpretable Joint-AI assisted diagnosis | Experimental | Diagnostic Test: The assistance of the interpretable Joint-AI modelEndoscopists diagnose pancreatic solid lesions based on endoscopic ultrasound images, clinical data, predictions given by the Joint-AI, and interpretability analysis results used to improve the transparency of the decision-making process of the Joint-AI model. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (2 listed)
- Imaging examinations (MRI, CT, B-ultrasound) show a solid mass in the pancreas, which requires endoscopic ultrasound guided-fine needle aspiration/biopsy (EUS-FNA/B) to clarify the nature of the lesion in patients.
- Written consent provided
Key exclusion criteria (1 listed)
- Age under 18 years old
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 |
|---|---|---|
| The assistance of the Joint-AI modelPredictions given by the Joint-AI model will be provided to the endoscopists during their diagnosis | Diagnostic test | Joint-AI assisted diagnosis |
| The assistance of the interpretable Joint-AI modelPredictions given by the Joint-AI model and the results of the interpretability analysis will be provided to the endoscopists during their diagnosis | Diagnostic test | Interpretable Joint-AI assisted diagnosis |
Primary outcome
Rate of correct diagnostic classification with assistance of the Joint-AI Model
Time frame: Through study completion, an average of 1 year
The rate of correct diagnoses in discriminating pancreatic cancer from other non-cancer lesions, determined by comparing endoscopist diagnosis assisted by the Joint-AI model against the final histopathological diagnosis (reference standard).
Rate of correct diagnostic classification with assistance of the Interpretable Joint-AI Model
Time frame: Through study completion, an average of 1 year
The rate of correct diagnoses in discriminating pancreatic cancer from other non-cancer lesions, determined by comparing endoscopist assessments assisted by the Interpretable Joint-AI model against the final histopathological diagnosis (reference standard)
3 secondary outcomes
Rate of correct diagnostic classification of the Joint-AI model and the interpretable Joint-AI model
Time frame: Through study completion, an average of 1 year
Endoscopist-reported confidence score in diagnosis with AI assistance (the score is on a scale of 0%-100%, where 0 represents "not confident at all" and 100 represents "completely confident")
Time frame: Through study completion, an average of 1 year
Rate of correct diagnostic classification of endoscopists without AI assistance
Time frame: Through study completion, an average of 1 year
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 31 Dec 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.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing The assistance of the Joint-AI model and The assistance of the interpretable Joint-AI model in people with Pancreatic Cancer and Pancreatitis, run by Huazhong University of Science and Technology 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 2 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: opening soon, as of the sponsor's last update on 31 Dec 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?
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 Bin Cheng (86-13986097542, b.cheng@tjh.tjmu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Rate of correct diagnostic classification with assistance of the Joint-AI Model (time frame: Through study completion, an average of 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 31 Dec 2024.