AI Assist in the Diagnosis of Pancreatic Solid LesionsNCT05706415
AI Assisted the Diagnosis of Pancreatic Solid Lesions
An observational study of AI Assist in the Diagnosis of Pancreatic Solid Lesions, run by Changhai Hospital at 2 sites in China.
Trial summary
Solid lesions of the pancreas mainly include tumor and non tumor lesions. More than 90% of pancreatic tumor lesions are pancreatic cancer, which is characterized by high mortality and poor prognosis and requires surgical treatment; Non-tumor lesions of the pancreas are mainly inflammatory lesions, which usually do not… Read more about this study
- Who is it for?
- Ages 18 to 75 years
- Conditions the study is for
- AI Assist in the Diagnosis of Pancreatic Solid Lesions
- What is being tested?
- Clinicians will review the suggestions of a hypothetical AI
- How many people
- 200 participants
- Where
- 2 sitesChina
- Study dates
- 21 Jan 2023to 21 Feb 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 to 75 years
- All sexes
- Adult, Older adult
Study population: Patients who had pancreatic solid mass will be enrolled in our study.
Who may be able to join
- pancreatic solid mass in CT and EUS
Who cannot join
- insufficient imaging quality of CT or EUS
- endoscopic ultrasound non accessible lesions
Trial locations and contacts
2 sites in 1 country.
China2 sites, 2 recruiting
- Recruiting
CT and EUS
Shanghai, Shanghai Municipality, 200433
Zhaoshen Li, +86-021-31161354, sky110319@163.com
- Recruiting
CT and EUS
Shanghai, Shanghai Municipality, 200433
Zhaoshen Li, 86-21-31162338, changhaiec@126.com
About this study
Official title: Enhanced Deep Learning Model for Diagnosis of Pancreatic Solid Lesions Through Multimodal Clinical Images
Solid lesions of the pancreas mainly include tumor and non tumor lesions. More than 90% of pancreatic tumor lesions are pancreatic cancer, which is characterized by high mortality and poor prognosis and requires surgical treatment; Non-tumor lesions of the pancreas are mainly inflammatory lesions, which usually do not require surgical treatment, but can be treated with drugs. The common ones are chronic pancreatitis and autoimmune pancreatitis, with a good prognosis. Clinically, the differential diagnosis between them is very difficult. Multi-disciplinary diagnosis and treatment of MDT makes our understanding of pancreatic diseases increasingly rich and in-depth. From disease diagnosis to preoperative evaluation and curative effect evaluation, non-invasive imaging involves almost every link under MDT mode. In view of this, improving the differential diagnosis of pancreatic solid space-occupying lesions on imaging will be more conducive to the diagnosis and treatment under MDT mode, so new technologies such as artificial intelligence should be considered. Our goal is to develop a clinically applicable artificial intelligence system, which uses multiple modes to simulate the routine clinical workflow and assist in the diagnosis of benign and malignant pancreatic solid space-occupying lesions.
Read the detailed description
The diagnosis of solid pancreatic lesions is challenging, MDT is a very effective method, but it has a certain misdiagnosis rate. This is a multi-center, prospective and observational clinical study. Our goal is to develop a clinically applicable artificial intelligence system. On the one hand, our artificial intelligence based on clinical data+CT imaging images can assist MDT doctors to diagnose the nature of pancreatic space-occupying lesions and reduce misdiagnosis; On the other hand, if a patient needs EUS-FNA puncture, the multimodal artificial intelligence system based on clinical data+CT+EUS developed by us can help MDT doctors understand the nature of pancreatic space-occupying lesions and reduce the probability of misdiagnosis or secondary puncture.
- Being tested
- Clinicians will review the suggestions of a hypothetical AI
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Clinicians will review the suggestions of a hypothetical AIDiagnostic test
There is no intervention. Clinicians will review the suggestions of a hypothetical AI
| Study group | Role | What they receive |
|---|---|---|
| patients with solid lesions of pancreas | Not described Receives: Diagnostic Test: Clinicians will review the suggestions of a hypothetical AI |
What is being measured
The primary outcome is the main question the study is designed to answer.
Researchers use artificial intelligence (AI) support system to assist in diagnosis of pancreatic solid space-occupying lesions
A multi-layer screening deep convolution network based on deep convolution network was developed to observe its accuracy, sensitivity and specificity in assisting MDT doctors to identify benign and malignant pancreatic space-occupying lesions.
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 posted31 Jan 2023
- Study start21 Jan 2023 (estimated)
- Primary completion21 Feb 2023 (estimated)
- Study completion21 Feb 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for AI Assist in the Diagnosis of Pancreatic Solid LesionsStudies of the same condition, open and under way.
- More trials run by Changhai HospitalThe 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 crossover
- Time perspective
- Prospective
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Changhai Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- The Third People's Hospital of Chengdu, Ruijin Hospital, Sun Yat-sen University, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05706415
- Sponsor's protocol ID
- 2022-AI and MDT
- Status last verified
- Jan 2023
- First submitted
- 21 Jan 2023
- Last updated
- 31 Jan 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 31 Jan 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Enhanced Deep Learning Model for Diagnosis of Pancreatic Solid Lesions Through Multimodal Clinical Images
- ClinicalTrials.gov ID
- NCT05706415
- Acronym / sponsor ID
- 2022-AI and MDT
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Changhai HospitalUniversity, hospital or other
- Collaborators
- The Third People's Hospital of Chengdu, Ruijin Hospital, Sun Yat-sen University, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
- Study start
- 21 Jan 2023 (estimated)
- Primary completion
- 21 Feb 2023 (estimated)
- Study completion
- 21 Feb 2023 (estimated)
- First posted
- 31 Jan 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.
Solid lesions of the pancreas mainly include tumor and non tumor lesions. More than 90% of pancreatic tumor lesions are pancreatic cancer, which is characterized by high mortality and poor prognosis and requires surgical treatment; Non-tumor lesions of the pancreas are mainly inflammatory lesions, which usually do not require surgical treatment, but can be treated with drugs. The common ones are chronic pancreatitis and autoimmune pancreatitis, with a good prognosis. Clinically, the differential diagnosis between them is very difficult. Multi-disciplinary diagnosis and treatment of MDT makes our understanding of pancreatic diseases increasingly rich and in-depth. From disease diagnosis to preoperative evaluation and curative effect evaluation, non-invasive imaging involves almost every link under MDT mode. In view of this, improving the differential diagnosis of pancreatic solid space-occupying lesions on imaging will be more conducive to the diagnosis and treatment under MDT mode, so new technologies such as artificial intelligence should be considered. Our goal is to develop a clinically applicable artificial intelligence system, which uses multiple modes to simulate the routine clinical workflow and assist in the diagnosis of benign and malignant pancreatic solid space-occupying lesions.
Detailed description
The diagnosis of solid pancreatic lesions is challenging, MDT is a very effective method, but it has a certain misdiagnosis rate. This is a multi-center, prospective and observational clinical study. Our goal is to develop a clinically applicable artificial intelligence system. On the one hand, our artificial intelligence based on clinical data+CT imaging images can assist MDT doctors to diagnose the nature of pancreatic space-occupying lesions and reduce misdiagnosis; On the other hand, if a patient needs EUS-FNA puncture, the multimodal artificial intelligence system based on clinical data+CT+EUS developed by us can help MDT doctors understand the nature of pancreatic space-occupying lesions and reduce the probability of misdiagnosis or secondary puncture.
Study design
- Observational model
- Case crossover
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| patients with solid lesions of pancreas | – | Diagnostic Test: Clinicians will review the suggestions of a hypothetical AI |
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- Patients who had pancreatic solid mass will be enrolled in our study.
Key inclusion criteria (1 listed)
- pancreatic solid mass in CT and EUS
Key exclusion criteria (2 listed)
- insufficient imaging quality of CT or EUS
- endoscopic ultrasound non accessible lesions
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 |
|---|---|---|
| Clinicians will review the suggestions of a hypothetical AIThere is no intervention. Clinicians will review the suggestions of a hypothetical AI | Diagnostic test | patients with solid lesions of pancreas |
Primary outcome
Researchers use artificial intelligence (AI) support system to assist in diagnosis of pancreatic solid space-occupying lesions
Time frame: 2 months
A multi-layer screening deep convolution network based on deep convolution network was developed to observe its accuracy, sensitivity and specificity in assisting MDT doctors to identify benign and malignant pancreatic space-occupying lesions.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 31 Jan 2023.
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.
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of AI Assist in the Diagnosis of Pancreatic Solid Lesions, run by Changhai Hospital 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 1 inclusion and 2 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 31 Jan 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?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Researchers use artificial intelligence (AI) support system to assist in diagnosis of pancreatic solid space-occupying lesions (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 31 Jan 2023.