Ovarian MassNCT07801209
An AI-Assisted Agentic System for Ultrasound Scanning and Diagnosis of Ovarian Lesions
An observational study of Ovarian Mass and Adnexal Mass, run by Women's Hospital School Of Medicine Zhejiang University at 2 sites in China.
Trial summary
Investigators developed an interactive agentic system designed to guide newly qualified sonographers in ovarian lesion scanning and improve their scanning quality and diagnostic performance toward expert-level standards. Read more about this study
- Who is it for?
- Ages 18 to 75 years, women only
- Conditions the study is for
- Ovarian Mass, Adnexal Mass
- What is being tested?
- non-AI assisted and then AI assisted and AI assisted and then non-AI assisted
- How many people
- 250 participants
- Where
- 2 sitesChina
- Study dates
- 19 Aug 2026to 31 Mar 2027
- How long will I take part?
- 3 Months
- 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 18 to 75 years
- Women only
- No healthy volunteers
- Adult, Older adult
Study population: Participants, with suspected ovarian masses identified clinically or by previous imaging examination, will be consecutively recruited from gynecological ultrasound clinics of tertiary cancer centers and primary healthcare institutions across China.
Who may be able to join
- Female aged 18-75 years
- With suspected ovarian masses identified clinically or by previous imaging examination
- Eligible for and able to undergo transvaginal or transabdominal ultrasonography
- Agree to provide written informed consent before enrollment
Who cannot join
- No ovarian mass identified on ultrasound examination
- Previous bilateral oophorectomy
- Previous surgery or chemotherapy for ovarian cancer
- Previous treatment for other malignant tumors
- Presence of any psychiatric or psychological disorders that may prevent completion of the study procedures or follow-up
- Concurrent participation in other clinical trials that may interfere with the outcomes of this study
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
Ruixia Dai, Ph.D. student contact
China2 sites
Women's Hospital School Of Medicine Zhejiang University
Hangzhou, Zhejiang, 310006
Jiale Qin, Prof., Professor, +86 0571-89998869, qinjiale@zju.edu.cn
Ruixia Dai, Ph.D. student, 12418399@zju.edu.cn
Shaoxing Maternity and Child Health Care Hospital
Shaoxing, Zhejiang, 311800
Hua Yuan, Chief physician in ultrasound, +86 0575-88211352, yh0464@163.com
Xuezhen Lou, Attending physician, 505571621@qq.com
About this study
Official title: New Strategy of Knowledge-Enhanced Large Model for Ultrasound Scanning and Diagnosis of Ovarian Masses
Investigators developed an interactive agentic system designed to guide newly qualified sonographers in ovarian lesion scanning and improve their scanning quality and diagnostic performance toward expert-level standards. Our agentic system is capable of capturing key features including the max-diameter plane of ovarian lesions from dynamic ultrasound videos, translating these findings into standardized International Ovarian Tumor Analysis (IOTA) descriptors, and providing multi-turn guidance for subsequent scanning, and ultimately generating an AI-assisted diagnostic assessment based on embedded expert knowledge.
In this multicenter study, participants are asked to undergo gynecological ultrasonography performed by sonographers with less than 3 years of experience with or without AI assistance. Our researchers will compare the performance of operators working with AI against that of operators working without AI, as well as against the performance of expert sonographers, to see whether AI assistance enhances the proficiency of less experienced operators and help them approach the scanning quality and diagnostic accuracy of expert sonographers in real-world clinical scenarios.
Read the detailed description
This multicenter, prospective study will be conducted at tertiary cancer centers and primary healthcare institutions across China. Participants will be recruited from gynecological ultrasound clinics of each site.
Each participant will undergo gynecological ultrasonography under both AI-assisted and unassisted conditions according to their group allocation. Following completion of the study examinations, expert sonographers with more than 10 years of experience, blinded to the scanning and diagnostic results of the junior sonographers, will independently perform a repeat gynecological ultrasound examination of each participant. Based solely on their independent examination, they will issue the final clinical report for each participant. The expert assessments will serve as one reference standard for evaluating scanning completeness, feature interpretation accuracy and diagnostic agreement. Histopathological findings, when available, or clinical follow-up for conservatively managed lesions will serve as the reference standard for evaluating diagnostic accuracy.
- Conditions
- Ovarian Mass, Adnexal Mass
- Being tested
- non-AI assisted and then AI assisted, AI assisted and then non-AI assisted
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
non-AI assisted and then AI assistedOther
Participants first undergo ultrasound scanning and diagnosis by a junior sonographer without AI assistance, followed by ultrasound scanning by the same sonographer with AI assistance.
AI assisted and then non-AI assistedOther
Participants first undergo ultrasound scanning and diagnosis by a junior sonographer with AI assistance, followed by ultrasound scanning by another junior sonographer without AI assistance. The two junior sonographers are blinded to each other's scanning and assessment results.
| Study group | Role | What they receive |
|---|---|---|
| Arm A: experimental sequence of non-AI exertion followed by AI-assisted exertion | The goal is to test the within-operator effect associated with AI assistance by comparing the performance of the same junior sonographer before and after AI guidance, and to compare the performance of both AI-assisted and unaided junior sonographers with that of the standalone AI model. Receives: Other: non-AI assisted and then AI assisted | |
| Arm B: experimental sequence of AI-assisted exertion followed by non-AI exertion | The goal is to test the clinical utility of AI with minimized potential carry-over effects and recall bias caused by repeated examinations on the same patient, by comparing the performance of one junior sonographer with AI-assisted, with that of independent unaided junior sonographers, and by comparing the performance of both AI-assisted junior sonographers and another unaided ones with that of the standalone AI model. Receives: Other: AI assisted and then non-AI assisted |
What is being measured
The primary outcome is the main question the study is designed to answer.
Completeness of ultrasound feature acquisition
The completeness of ultrasound feature acquisition will be assessed with reference to national authoritative quality-control standards. After each examination, all stored ultrasound images and videos will be labeled according to their intended purpose. A feature will be considered adequately acquired when at least one stored image or video provides sufficient visual evidence for assessment of that feature. The proportion of required features successfully acquired will be calculated. The proportion of redundant stored images will also be recorded as an additional indicator of acquisition quality.
Measured: Up to 7 days from completion of the study
Accuracy of interpretation of individual ultrasound features
The accuracy of interpretation of ultrasound features will be assessed by comparing the assessments made by junior sonographers under AI-assisted and unassisted conditions with the findings independently acquired and interpreted by expert sonographers during a separate repeat ultrasound examination. The accuracy rate for each feature will be calculated.
Measured: Up to 7 days from completion of the study
Agreement between junior and expert sonographers in ultrasound diagnosis
Agreement between junior and expert sonographers will be assessed for the final diagnosis of ovarian lesions. The assessments made by junior sonographers under AI-assisted and unassisted conditions will be compared with the corresponding diagnoses independently obtained by expert sonographers during a separate repeat ultrasound examination, which will then be quantified using appropriate agreement statistics.
Measured: Up to 7 days from completion of the study
Diagnostic accuracy of ovarian tumors
Diagnostic accuracy will be assessed against histopathological diagnosis as the golden reference standard. This outcome will be evaluated only in participants who undergo surgical treatment and have definitive histopathological findings based on paraffin-embedded surgical specimens.
Measured: Within 3 months after the ultrasonography examination
Diagnostic performance of ovarian tumors
Area under curve will be assessed against histopathological diagnosis as the golden reference standard. This outcome will be evaluated only in participants who undergo surgical treatment and have definitive histopathological findings based on paraffin-embedded surgical specimens.
Measured: Within 3 months after the ultrasonography examination
Diagnostic performance of ovarian tumors
Diagnostic sensitivity and specificity will be assessed against histopathological diagnosis as the golden reference standard. This outcome will be evaluated only in participants who undergo surgical treatment and have definitive histopathological findings based on paraffin-embedded surgical specimens.
Measured: Within 3 months after the ultrasonography examination
Diagnostic performance of ovarian tumors
Diagnostic F1-score will be assessed against histopathological diagnosis as the golden reference standard. This outcome will be evaluated only in participants who undergo surgical treatment and have definitive histopathological findings based on paraffin-embedded surgical specimens.
Measured: Within 3 months after the ultrasonography examination
Secondary outcomes (1)
Confidence in ultrasound feature acquisition and diagnosis
Sonographers' confidence in the completeness and adequacy of feature acquisition and in their final diagnostic assessment will be evaluated using a predefined rating scale as follows: 5 = very confident, 4 = confident, 3 = uncertain, 2 = less confident, and 1 = not confident at al.
Measured: Up to 7 days from completion of the study
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Sep 2026
- Study start19 Aug 2026 (estimated)
- Primary completion31 Mar 2027 (estimated)
- Study completion31 Mar 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Ovarian MassStudies 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 250 participants (estimated)
- Target duration
- 3 Months
Sponsor and investigators
- Lead sponsor
- Women's Hospital School Of Medicine Zhejiang University
- Sponsor type
- University, hospital or other
- Collaborators
- Shaoxing Maternity and Child Health Care Hospital
- Responsible party
- Principal investigator, Jiale Qin, Women's Hospital School Of Medicine Zhejiang University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07801209
- Sponsor's protocol ID
- IRB-20250490-R
- Status last verified
- Sep 2026
- First submitted
- 17 Aug 2026
- Last updated
- 3 Sep 2026
- Results posted
- No
- Data monitoring committee
- Yes
- 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 3 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- New Strategy of Knowledge-Enhanced Large Model for Ultrasound Scanning and Diagnosis of Ovarian Masses
- ClinicalTrials.gov ID
- NCT07801209
- Acronym / sponsor ID
- IRB-20250490-R
- Conditions
- Ovarian Mass, Adnexal Mass
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 250 participants (estimated)
- Lead sponsor
- Women's Hospital School Of Medicine Zhejiang UniversityUniversity, hospital or other
- Collaborators
- Shaoxing Maternity and Child Health Care Hospital
- Study start
- 19 Aug 2026 (estimated)
- Primary completion
- 31 Mar 2027 (estimated)
- Study completion
- 31 Mar 2027 (estimated)
- First posted
- 3 Sep 2026 (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.
Investigators developed an interactive agentic system designed to guide newly qualified sonographers in ovarian lesion scanning and improve their scanning quality and diagnostic performance toward expert-level standards. Our agentic system is capable of capturing key features including the max-diameter plane of ovarian lesions from dynamic ultrasound videos, translating these findings into standardized International Ovarian Tumor Analysis (IOTA) descriptors, and providing multi-turn guidance for subsequent scanning, and ultimately generating an AI-assisted diagnostic assessment based on embedded expert knowledge.
In this multicenter study, participants are asked to undergo gynecological ultrasonography performed by sonographers with less than 3 years of experience with or without AI assistance. Our researchers will compare the performance of operators working with AI against that of operators working without AI, as well as against the performance of expert sonographers, to see whether AI assistance enhances the proficiency of less experienced operators and help them approach the scanning quality and diagnostic accuracy of expert sonographers in real-world clinical scenarios.
Detailed description
This multicenter, prospective study will be conducted at tertiary cancer centers and primary healthcare institutions across China. Participants will be recruited from gynecological ultrasound clinics of each site.
Each participant will undergo gynecological ultrasonography under both AI-assisted and unassisted conditions according to their group allocation. Following completion of the study examinations, expert sonographers with more than 10 years of experience, blinded to the scanning and diagnostic results of the junior sonographers, will independently perform a repeat gynecological ultrasound examination of each participant. Based solely on their independent examination, they will issue the final clinical report for each participant. The expert assessments will serve as one reference standard for evaluating scanning completeness, feature interpretation accuracy and diagnostic agreement. Histopathological findings, when available, or clinical follow-up for conservatively managed lesions will serve as the reference standard for evaluating diagnostic accuracy.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 3 Months
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Arm A: experimental sequence of non-AI exertion followed by AI-assisted exertion | – | Other: non-AI assisted and then AI assistedThe goal is to test the within-operator effect associated with AI assistance by comparing the performance of the same junior sonographer before and after AI guidance, and to compare the performance of both AI-assisted and unaided junior sonographers with that of the standalone AI model. |
| Arm B: experimental sequence of AI-assisted exertion followed by non-AI exertion | – | Other: AI assisted and then non-AI assistedThe goal is to test the clinical utility of AI with minimized potential carry-over effects and recall bias caused by repeated examinations on the same patient, by comparing the performance of one junior sonographer with AI-assisted, with that of independent unaided junior sonographers, and by comparing the performance of both AI-assisted junior sonographers and another unaided ones with that of the standalone AI model. |
Who may qualify
- Ages
- 18 to 75 years
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Participants, with suspected ovarian masses identified clinically or by previous imaging examination, will be consecutively recruited from gynecological ultrasound clinics of tertiary cancer centers and primary healthcare institutions across China.
Key inclusion criteria (4 listed)
- Female aged 18-75 years
- With suspected ovarian masses identified clinically or by previous imaging examination
- Eligible for and able to undergo transvaginal or transabdominal ultrasonography
- Agree to provide written informed consent before enrollment
Key exclusion criteria (6 listed)
- No ovarian mass identified on ultrasound examination
- Previous bilateral oophorectomy
- Previous surgery or chemotherapy for ovarian cancer
- Previous treatment for other malignant tumors
- Presence of any psychiatric or psychological disorders that may prevent completion of the study procedures or follow-up
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 |
|---|---|---|
| non-AI assisted and then AI assistedParticipants first undergo ultrasound scanning and diagnosis by a junior sonographer without AI assistance, followed by ultrasound scanning by the same sonographer with AI assistance. | Other | Arm A: experimental sequence of non-AI exertion followed by AI-assisted exertion |
| AI assisted and then non-AI assistedParticipants first undergo ultrasound scanning and diagnosis by a junior sonographer with AI assistance, followed by ultrasound scanning by another junior sonographer without AI assistance. The two junior sonographers are blinded to each other's scanning and assessment results. | Other | Arm B: experimental sequence of AI-assisted exertion followed by non-AI exertion |
Primary outcome
Completeness of ultrasound feature acquisition
Time frame: Up to 7 days from completion of the study
The completeness of ultrasound feature acquisition will be assessed with reference to national authoritative quality-control standards. After each examination, all stored ultrasound images and videos will be labeled according to their intended purpose. A feature will be considered adequately acquired when at least one stored image or video provides sufficient visual evidence for assessment of that feature. The proportion of required features successfully acquired will be calculated. The proportion of redundant stored images will also be recorded as an additional indicator of acquisition quality.
Accuracy of interpretation of individual ultrasound features
Time frame: Up to 7 days from completion of the study
The accuracy of interpretation of ultrasound features will be assessed by comparing the assessments made by junior sonographers under AI-assisted and unassisted conditions with the findings independently acquired and interpreted by expert sonographers during a separate repeat ultrasound examination. The accuracy rate for each feature will be calculated.
Agreement between junior and expert sonographers in ultrasound diagnosis
Time frame: Up to 7 days from completion of the study
Agreement between junior and expert sonographers will be assessed for the final diagnosis of ovarian lesions. The assessments made by junior sonographers under AI-assisted and unassisted conditions will be compared with the corresponding diagnoses independently obtained by expert sonographers during a separate repeat ultrasound examination, which will then be quantified using appropriate agreement statistics.
Diagnostic accuracy of ovarian tumors
Time frame: Within 3 months after the ultrasonography examination
Diagnostic accuracy will be assessed against histopathological diagnosis as the golden reference standard. This outcome will be evaluated only in participants who undergo surgical treatment and have definitive histopathological findings based on paraffin-embedded surgical specimens.
Diagnostic performance of ovarian tumors
Time frame: Within 3 months after the ultrasonography examination
Area under curve will be assessed against histopathological diagnosis as the golden reference standard. This outcome will be evaluated only in participants who undergo surgical treatment and have definitive histopathological findings based on paraffin-embedded surgical specimens.
Diagnostic performance of ovarian tumors
Time frame: Within 3 months after the ultrasonography examination
Diagnostic sensitivity and specificity will be assessed against histopathological diagnosis as the golden reference standard. This outcome will be evaluated only in participants who undergo surgical treatment and have definitive histopathological findings based on paraffin-embedded surgical specimens.
Diagnostic performance of ovarian tumors
Time frame: Within 3 months after the ultrasonography examination
Diagnostic F1-score will be assessed against histopathological diagnosis as the golden reference standard. This outcome will be evaluated only in participants who undergo surgical treatment and have definitive histopathological findings based on paraffin-embedded surgical specimens.
1 secondary outcome
Confidence in ultrasound feature acquisition and diagnosis
Time frame: Up to 7 days from completion of the study
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 3 Sep 2026.
- 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.
Ruixia Dai, Ph.D. student contact
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 Ovarian Mass and Adnexal Mass, run by Women's Hospital School Of Medicine Zhejiang 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 4 inclusion and 6 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 3 Sep 2026. 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?
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 Jiale Qin, Prof., Professor (+86 0571-89998869, qinjiale@zju.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Completeness of ultrasound feature acquisition (time frame: Up to 7 days from completion of the study). The study lists 7 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 3 Sep 2026.