Ovarian NeoplasmsNCT06528236
Research and Application of Ultrasonic Intelligent Diagnosis System for Ovarian Mass
An observational study of Ovarian Neoplasms and Adnexal Mass, run by Zhejiang Provincial People's Hospital.
Trial summary
Research on automatic detection of ovarian mass and intelligent auxiliary diagnosis system based on multimodal ultrasound images. Read more about this study
- Who is it for?
- Any age, women onlyHealthy volunteers can take part
- Conditions the study is for
- Ovarian Neoplasms, Adnexal Mass
- What is being tested?
- Artificial intelligence model
- How many people
- 100,000 participants
- Where
- No sites listed yet
- Study dates
- 30 Jul 2024to 30 Jul 2029
- Phase
- Observational
- 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 Any age
- Women only
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: During gynecological ultrasound examination, at least one patient with persistent ovarian tumor was found. The patient underwent surgical treatment and the histopathological results.
Who may be able to join
- During gynecological ultrasound examination, at least one patient with persistent ovarian tumor was found.
- The patient underwent surgical treatment and the histopathological results.
Who cannot join
- Histopathological analysis confirms non-ovarian tumor;
- Histopathological results are inconclusive;
- Issues with image quality: the ovarian mass is incomplete and does not show some surrounding tissues (but the mass is too large to exclude completely); the images are overly blurry, making it difficult to determine the characteristics of the ovarian mass (possible reasons include hardware quality issues with the ultrasound machine, motion blur, focusing problems, presence of intestinal gas in the patient); gain settings make it difficult to judge the characteristics of the ovarian mass (such as low contrast, excessively dark images, or saturation); the presence of artifacts affects the assessment of ultrasound characteristics of the ovarian mass and should be excluded.
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: Research on Automatic Detection of Ovarian Mass and Intelligent Auxiliary Diagnosis System Based on Multimodal Ultrasound Images
Research on automatic detection of ovarian mass and intelligent auxiliary diagnosis system based on multimodal ultrasound images.
Read the detailed description
Investigators aimed to develop an ultrasonic intelligent diagnosis system for ovarian mass based on multimodal ultrasound images.
- Conditions
- Ovarian Neoplasms, Adnexal Mass
- Being tested
- Artificial intelligence model
- Medical subject headings
- Ovarian Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial intelligence modelDiagnostic test
Using the artificial intelligence model to diagnosis benign, borderline, and malignant ovarian masses.
| Study group | Role | What they receive |
|---|---|---|
| Training cohort | Training cohort is used to training artificial model based on multimodel ultrasound images or videos. | |
| Validation cohort | Validation cohort is used to validate artificial model. Receives: Diagnostic Test: Artificial intelligence model | |
| Internal test cohort | Internal test cohort is used to internally test artificial model. Receives: Diagnostic Test: Artificial intelligence model | |
| External test cohort | External test cohort is used to internally test artificial model. Receives: Diagnostic Test: Artificial intelligence model |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area under the curve
AUC (Area Under the Curve) is a common index used to evaluate the performance of binary classification model.
Measured: Through study completion, an average of 1 year
Secondary outcomes (1)
Sensitivity
Sensitivity refers to the ability of the test to correctly identify a positive result in an individual who actually has the disease. It represents the proportion of cases in which the test is able to detect a positive for the disease
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 posted30 Jul 2024
- Study start30 Jul 2024 (estimated)
- Primary completion30 Jul 2029 (estimated)
- Study completion30 Jul 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Ovarian NeoplasmsStudies 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
- Other
- Enrolment
- 100,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Zhejiang Provincial People's Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- The Second Affiliated Hospital of Harbin Medical University, Sichuan provincial maternity and child health care hospital, The Affiliated Hospital of Qingdao University, Aksu First People's Hospital, Women's Hospital School Of Medicine Zhejiang University
- Responsible party
- Sponsor
- Investigators
- Litao Sun, Professor, Study chair, Zhejiang Provincial People's Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06528236
- Sponsor's protocol ID
- KY2024053
- Status last verified
- Jul 2024
- First submitted
- 18 Jul 2024
- Last updated
- 30 Jul 2024
- Results posted
- No
- Data monitoring committee
- 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 30 Jul 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Research on Automatic Detection of Ovarian Mass and Intelligent Auxiliary Diagnosis System Based on Multimodal Ultrasound Images
- ClinicalTrials.gov ID
- NCT06528236
- Acronym / sponsor ID
- KY2024053
- Conditions
- Ovarian Neoplasms, Adnexal Mass
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 100,000 participants (estimated)
- Lead sponsor
- Zhejiang Provincial People's HospitalUniversity, hospital or other
- Collaborators
- The Second Affiliated Hospital of Harbin Medical University, Sichuan provincial maternity and child health care hospital, The Affiliated Hospital of Qingdao University, Aksu First People's Hospital, Women's Hospital School Of Medicine Zhejiang University
- Study start
- 30 Jul 2024 (estimated)
- Primary completion
- 30 Jul 2029 (estimated)
- Study completion
- 30 Jul 2029 (estimated)
- First posted
- 30 Jul 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.
Research on automatic detection of ovarian mass and intelligent auxiliary diagnosis system based on multimodal ultrasound images.
Detailed description
Investigators aimed to develop an ultrasonic intelligent diagnosis system for ovarian mass based on multimodal ultrasound images.
Study design
- Observational model
- Other
- Time perspective
- Other
- Number of arms
- 4
| Arm | Type | Receives |
|---|---|---|
| Training cohort | – | –Training cohort is used to training artificial model based on multimodel ultrasound images or videos. |
| Validation cohort | – | Diagnostic Test: Artificial intelligence modelValidation cohort is used to validate artificial model. |
| Internal test cohort | – | Diagnostic Test: Artificial intelligence modelInternal test cohort is used to internally test artificial model. |
| External test cohort | – | Diagnostic Test: Artificial intelligence modelExternal test cohort is used to internally test artificial model. |
Who may qualify
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- During gynecological ultrasound examination, at least one patient with persistent ovarian tumor was found. The patient underwent surgical treatment and the histopathological results.
Key inclusion criteria (2 listed)
- During gynecological ultrasound examination, at least one patient with persistent ovarian tumor was found.
- The patient underwent surgical treatment and the histopathological results.
Key exclusion criteria (3 listed)
- Histopathological analysis confirms non-ovarian tumor;
- Histopathological results are inconclusive;
- Issues with image quality: the ovarian mass is incomplete and does not show some surrounding tissues (but the mass is too large to exclude completely); the images are overly blurry, making it difficult to determine the characteristics of the ovarian mass (possible reasons include hardware quality issues with the ultrasound machine, motion blur, focusing problems, presence of intestinal gas in the patient); gain settings make it difficult to judge the characteristics of the ovarian mass (such as low contrast, excessively dark images, or saturation); the presence of artifacts affects the assessment of ultrasound characteristics of the ovarian mass and should be excluded.
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 modelUsing the artificial intelligence model to diagnosis benign, borderline, and malignant ovarian masses. | Diagnostic test | External test cohort, Internal test cohort, Validation cohort |
Primary outcome
Area under the curve
Time frame: Through study completion, an average of 1 year
AUC (Area Under the Curve) is a common index used to evaluate the performance of binary classification model.
1 secondary outcome
Sensitivity
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 30 Jul 2024.
- 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.
Litao Sun, Professor study chair
Zhejiang Provincial People's Hospital
Questions about this trial
What is this clinical trial studying?
An observational study of Ovarian Neoplasms and Adnexal Mass, run by Zhejiang Provincial People's Hospital.
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 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: opening soon, as of the sponsor's last update on 30 Jul 2024. Status can change between updates, so confirm with the study contact before making plans.
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 Yingnan Wu, Doctor (0086 19883106164, litaosun1971@sina.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area under the curve (time frame: Through study completion, an average of 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 30 Jul 2024; AzTrials last refreshed its copy on 8 Nov 2026.