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An AI-Assisted Agentic System for Ultrasound Scanning and Diagnosis of Ovarian Lesions
Sponsor: Women's Hospital School Of Medicine Zhejiang University
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. 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.
Official title: New Strategy of Knowledge-Enhanced Large Model for Ultrasound Scanning and Diagnosis of Ovarian Masses
Key Details
Gender
FEMALE
Age Range
18 Years - 75 Years
Study Type
OBSERVATIONAL
Enrollment
250
Start Date
2026-08-19
Completion Date
2027-03-31
Last Updated
2026-09-03
Healthy Volunteers
No
Conditions
Interventions
non-AI assisted and then AI assisted
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 assisted
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.
Locations (2)
Women's Hospital School Of Medicine Zhejiang University
Hangzhou, Zhejiang, China
Shaoxing Maternity and Child Health Care Hospital
Shaoxing, Zhejiang, China