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.
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
No ovarian mass identified on ultrasound examination
Previous bilateral oophorectomy
Previous surgery or chemotherapy for ovarian cancer
Previous treatment for other malignant tumors
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.
Cohort
Prospective
non-AI assisted and then AI assisted
Other interventionParticipants 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 assisted
Other interventionParticipants 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.
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.
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.
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.
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.
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.
Click on the lead sponsor to view all of their trials.
Women's Hospital School Of Medicine Zhejiang University
Lead sponsor
Shaoxing Maternity and Child Health Care Hospital
Collaborator
This trial is not recruiting at the moment. You can still explore other options:
