发表机构
Academic Division of Gynecology and Obstetrics, University of Turin; Obstetric and Gynecology Unit, Ospedale Sant'Anna, Department of Surgical Sciences, University of Turin; Tel Aviv Sourasky Medical Center, Tel Aviv, Israel（妇科与产科学术部，都灵大学; 妇产科单元，圣安娜医院，外科科学系，都灵大学; 特拉维夫 Sourasky 医疗中心，特拉维夫，以色列）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对卵巢癌这一致命妇科恶性肿瘤，OvAi Focus软件可对妇科超声中的功能性卵巢和附件包块进行多类别语义分割，通过多中心数据集训练和验证，其分割DICE分数表现良好，优于同类先进方法。
AI 中文摘要
卵巢癌是最致命的妇科恶性肿瘤。由于操作者差异和形态复杂性，超声中附件包块和功能性卵巢的准确客观分割仍具有挑战性。我们展示了OvAi Focus，一款独立的人工智能软件医疗设备，可对功能性卵巢和附件包块进行多类别语义分割，区分囊性和实性成分。该系统在来自意大利和以色列6个中心的1081名成年女性的多中心数据集上进行了训练和独立验证。分割的DICE分数分别为：完整病变0.87、囊性0.85、实性0.68、功能性卵巢0.62，与同类先进方法相当或更优。
英文摘要
Ovarian cancer is the deadliest gynecological malignancy; accurate and objective segmentation of adnexal masses and functional ovaries in ultrasound (US) remains challenging due to operator variability and morphological complexity. We present OvAi Focus (SynDiag s.r.l., Italy), a stand-alone AI software medical device that performs multi-class semantic segmentation of functional ovaries and adnexal masses, distinguishing cystic from solid components. The system was trained and independently validated on a multicenter dataset of 1,081 adult women from 6 centers across Italy and Israel. Segmentation achieved DICE scores of 0.87 (complete lesion), 0.85 (cystic), 0.68 (solid), and 0.62 (functional ovary), in line with or superior to state-of-the-art approaches across heterogeneous acquisition settings.
CommentsAccepted for presentation at Ital-IA 2026 (6th CINI National Conference on Artificial Intelligence) in Rome, Italy. To be published in the CEUR-WS proceedings
