An AI-Assisted Agentic System for Ultrasound Scanning and Diagnosis of Ovarian Lesions

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.

Inclusion Criteria: * 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 Exclusion Criteria: * 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