Hepatic EchinococcosisNCT06540742
Multimodal Imaging Diagnosis and Decision Aid System for Hepatic Echinococcosis Based on Image Omics and Vision Macromodel
An observational study of Hepatic Echinococcosis, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University at 1 site in China.
Trial summary
Hepatic echinococcosis (hepatic echinococcosis) is an important zoonotic disease widely existing in the agricultural and pastoral areas of northwest China. The disease can be parasitic in any part of the human body and may affect multiple organs. In severe cases, patients will lose the ability to work. Read more about this study
- Who is it for?
- Ages 1 year to 80 years
- Conditions the study is for
- Hepatic Echinococcosis
- What is being tested?
- Artificial intelligence identifies liver hydatids
- How many people
- 1,000 participants
- Where
- 1 siteChina
- Study dates
- 30 Sep 2023to 12 Sep 2024
- 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 1 year to 80 years
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: In this study, based on the image data of patients with liver hydatid confirmed by pathology after surgery, deep learning technology was used to distinguish liver hydatid from hepatic cystic station lesions such as liver cyst and liver abscess
Who may be able to join
- Patients with complete original images in CT, ultrasound, and MRI dcim formats
- Patients with liver hydatid confirmed by pathology after operation
- Patients with complete clinical data preservation
Who cannot join
- Patients with poor quality imaging data
- Patients with incomplete clinical data
- CE4 and CE5 liver hydatid patients diagnosed by imaging alone without surgical treatment
Trial locations and contacts
1 site in 1 country.
China1 site
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Guangzhou, China/Guangdong
About this study
Hepatic echinococcosis (hepatic echinococcosis) is an important zoonotic disease widely existing in the agricultural and pastoral areas of northwest China. The disease can be parasitic in any part of the human body and may affect multiple organs. In severe cases, patients will lose the ability to work. At present, the disease faces challenges in diagnostic accuracy, specific type identification, preoperative activity assessment, postoperative recurrence prediction, and decision evaluation of T-tube indentation. This problem is particularly significant in high incidence areas with uneven distribution of medical resources and shortage of excellent imaging physicians and clinicians. Our previous studies have demonstrated that the use of visual large models and imaging omics algorithms can effectively segment liver echinococcus lesions, extract key features, and provide clinicians with accurate and reliable diagnosis and treatment recommendations. We believe that on the basis of the transformation of different medical image modes (such as MRI, CT and ultrasound) based on a broader multicentre large data set, the goal of effective identification, diagnosis, surgical decision support, and postoperative accurate prediction of hepatic echinococcosis can be achieved. We will use artificial intelligence technology solutions such as adversarial generation network, vision large model, image omics and decision level fusion, taking into account diagnosis and treatment efficiency, diagnosis and treatment automation and interpretability of diagnosis results, to build a comprehensive accurate diagnosis and prognosis system for hepatic echinococcosis
Read the detailed description
- Automatic recognition/Efficient diagnosis of hepatic Echinococcosis: Development of a deep learn-based AI diagnostic tool aimed at improving the differentiation of hepatic echinococcosis from other liver diseases such as liver cysts, liver abscesses, and other hepatic cystic space occupying lesions. The tool will utilize generative adversarial networks and polarized self-attention algorithms to effectively identify and classify hepatic echinococcosis to make up for the uneven medical resources and shortage of professional physicians in the western region
- Differential diagnosis of specific types of hepatic echinococcosis: To explore the use of multimodal imaging combined with deep learning methods to distinguish CL type, CE1 type and hepatic cyst of hepatic echinococcosis. This research will apply DINOv2 medical image segmentation algorithm and deep learning technology to accurately identify cases with relatively unevenly distributed and complex data.
- Prediction of postoperative recurrence of hepatic echinococcosis: Transfer learning and Deep-SVDD algorithm are used to predict the risk of postoperative recurrence of hepatic echinococcosis, providing an effective solution for unbalanced sample size data sets. In addition, by integrating the data on the medical big data platform, an AI-based postoperative recurrence prediction model was established
- Conditions
- Hepatic Echinococcosis
- Being tested
- Artificial intelligence identifies liver hydatids
- Keywords
- hepatic echinococcosis
- Medical subject headings
- Echinococcosis, Hepatic
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial intelligence identifies liver hydatidsDiagnostic test
Artificial neural network was constructed to automatically identify liver hydatid by using deep learning technology
| Study group | Role | What they receive |
|---|---|---|
| Observation group | In this study, preoperative image data of patients with hepatic hydatid were taken as the research object Receives: Diagnostic Test: Artificial intelligence identifies liver hydatids | |
| Control group | Hepatic cyst, hepatic abscess and normal liver were the control group Receives: Diagnostic Test: Artificial intelligence identifies liver hydatids |
What is being measured
The primary outcome is the main question the study is designed to answer.
roc curve
Receiver operating characteristic curve
Measured: 2024.7-2026.3
AUC
Area under the ROC curve
Measured: 2024.7-2026.3
PPV
Positive Predictive Value
Measured: 2024.7-2026.3
NPV
Negative Predictive Value
Measured: 2024.7-2026.3
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Aug 2024
- Study start30 Sep 2023
- Primary completion12 Sep 2024
- Study completion12 Sep 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Hepatic EchinococcosisStudies of the same condition, open and under way.
- More trials run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen 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
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 1,000 participants (actual)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Yajin Chen, Study director, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06540742
- Sponsor's protocol ID
- SYSKY-2024-517-01
- Status last verified
- Sep 2024
- First submitted
- 2 Aug 2024
- Last updated
- 19 Sep 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Relevant medical image data involves patient privacy, and the research group refused to share it
Publications
Wang Z, Bian H, Li J, Xu J, Fan H, Wu X, Cao Y, Guo B, Xu X, Wang H, Zhang L, Zhou H, Fan J, Ren Y, Geng Y, Feng X, Li L, Wei L, Zhang X. Detection and subtyping of hepatic echinococcosis from plain CT images with deep learning: a retrospective, multicentre study. Lancet Digit Health. 2023 Nov;5(11):e754-e762. doi: 10.1016/S2589-7500(23)00136-X. Epub 2023 Sep 26.
PubMed 37770335
Study at a glance
From the registry record, last updated by the sponsor on 19 Sep 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Multimodal Imaging Diagnosis and Decision Aid System for Hepatic Echinococcosis Based on Image Omics and Vision Macromodel
- ClinicalTrials.gov ID
- NCT06540742
- Acronym / sponsor ID
- SYSKY-2024-517-01
- Conditions
- Hepatic Echinococcosis
- Keywords
- hepatic echinococcosis
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,000 participants (actual)
- Lead sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityUniversity, hospital or other
- Study start
- 30 Sep 2023 (actual)
- Primary completion
- 12 Sep 2024 (actual)
- Study completion
- 12 Sep 2024 (actual)
- First posted
- 6 Aug 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.
Hepatic echinococcosis (hepatic echinococcosis) is an important zoonotic disease widely existing in the agricultural and pastoral areas of northwest China. The disease can be parasitic in any part of the human body and may affect multiple organs. In severe cases, patients will lose the ability to work. At present, the disease faces challenges in diagnostic accuracy, specific type identification, preoperative activity assessment, postoperative recurrence prediction, and decision evaluation of T-tube indentation. This problem is particularly significant in high incidence areas with uneven distribution of medical resources and shortage of excellent imaging physicians and clinicians. Our previous studies have demonstrated that the use of visual large models and imaging omics algorithms can effectively segment liver echinococcus lesions, extract key features, and provide clinicians with accurate and reliable diagnosis and treatment recommendations. We believe that on the basis of the transformation of different medical image modes (such as MRI, CT and ultrasound) based on a broader multicentre large data set, the goal of effective identification, diagnosis, surgical decision support, and postoperative accurate prediction of hepatic echinococcosis can be achieved. We will use artificial intelligence technology solutions such as adversarial generation network, vision large model, image omics and decision level fusion, taking into account diagnosis and treatment efficiency, diagnosis and treatment automation and interpretability of diagnosis results, to build a comprehensive accurate diagnosis and prognosis system for hepatic echinococcosis
Detailed description
- Automatic recognition/Efficient diagnosis of hepatic Echinococcosis: Development of a deep learn-based AI diagnostic tool aimed at improving the differentiation of hepatic echinococcosis from other liver diseases such as liver cysts, liver abscesses, and other hepatic cystic space occupying lesions. The tool will utilize generative adversarial networks and polarized self-attention algorithms to effectively identify and classify hepatic echinococcosis to make up for the uneven medical resources and shortage of professional physicians in the western region
- Differential diagnosis of specific types of hepatic echinococcosis: To explore the use of multimodal imaging combined with deep learning methods to distinguish CL type, CE1 type and hepatic cyst of hepatic echinococcosis. This research will apply DINOv2 medical image segmentation algorithm and deep learning technology to accurately identify cases with relatively unevenly distributed and complex data.
- Prediction of postoperative recurrence of hepatic echinococcosis: Transfer learning and Deep-SVDD algorithm are used to predict the risk of postoperative recurrence of hepatic echinococcosis, providing an effective solution for unbalanced sample size data sets. In addition, by integrating the data on the medical big data platform, an AI-based postoperative recurrence prediction model was established
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Observation group | – | Diagnostic Test: Artificial intelligence identifies liver hydatidsIn this study, preoperative image data of patients with hepatic hydatid were taken as the research object |
| Control group | – | Diagnostic Test: Artificial intelligence identifies liver hydatidsHepatic cyst, hepatic abscess and normal liver were the control group |
Who may qualify
- Ages
- 1 year to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- In this study, based on the image data of patients with liver hydatid confirmed by pathology after surgery, deep learning technology was used to distinguish liver hydatid from hepatic cystic station lesions such as liver cyst and liver abscess
Key inclusion criteria (3 listed)
- Patients with complete original images in CT, ultrasound, and MRI dcim formats
- Patients with liver hydatid confirmed by pathology after operation
- Patients with complete clinical data preservation
Key exclusion criteria (3 listed)
- Patients with poor quality imaging data
- Patients with incomplete clinical data
- CE4 and CE5 liver hydatid patients diagnosed by imaging alone without surgical treatment
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 identifies liver hydatidsArtificial neural network was constructed to automatically identify liver hydatid by using deep learning technology | Diagnostic test | Control group, Observation group |
Primary outcome
roc curve
Time frame: 2024.7-2026.3
Receiver operating characteristic curve
AUC
Time frame: 2024.7-2026.3
Area under the ROC curve
PPV
Time frame: 2024.7-2026.3
Positive Predictive Value
NPV
Time frame: 2024.7-2026.3
Negative Predictive Value
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 19 Sep 2024.
- Background: PubMed 37770335Wang Z, Bian H, Li J, Xu J, Fan H, Wu X, Cao Y, Guo B, Xu X, Wang H, Zhang L, Zhou H, Fan J, Ren Y, Geng Y, Feng X, Li L, Wei L, Zhang X. Detection and subtyping of hepatic echinococcosis from plain CT images with deep learning: a retrospective, multicentre study. Lancet Digit Health. 2023 Nov;5(11):e754-e762. doi: 10.1016/S2589-7500(23)00136-X. Epub 2023 Sep 26.
- This study has been registered in China\'s National Medical Research Registration and Archival Information SystemLink provided by the sponsor.
- Individual participant dataSharing planned: No. Relevant medical image data involves patient privacy, and the research group refused to share it
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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.
Yajin Chen study director
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen 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 Hepatic Echinococcosis, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen 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 3 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 19 Sep 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?
roc curve (time frame: 2024.7-2026.3). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06540742, which also lists 1 linked publication.
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 19 Sep 2024.