OvAi Focus: AI-based Multi-class Segmentation of Functional Ovaries and Adnexal Masses in Gynecological Ultrasound
Authors: Niccolò Tallone, Francesca Salis, Pio Raffaele Fina, Roberta Massobrio, Rosilari Bellacosa Marotti, Daniele Conti, Luca Fuso, Luca Mariani, +8 more
Organizations: SynDiag s.r.l., Turin, Italy · Academic Division of Gynecology and Obstetrics, University of Turin, Azienda Ospedaliera Ordine Mauriziano, Turin, Italy · University of Turin · Division of Gynaecology and Human Reproduction Physiopathology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy · Obstetric and Gynecology Unit, Ospedale Sant’Anna, Department of Surgical Sciences, University of Turin, Turin, Italy · Tel Aviv Sourasky Medical Center, Tel Aviv, Israel · Obstetric and Gynecology Unit, Presidio Ospedaliero Ospedale Martini, Turin, Italy · Obstetric and Gynecology Unit, Policlinico San Matteo di Pavia, Pavia, Italy
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.