Diagnostic performance of deep learning models on ultrasound images for distinguishing benign from malignant ovarian cysts.

Abstract

Background: Ovarian cysts are a common pelvic disorder in women, and accurate differentiation between benign and malignant types is essential for guiding treatment decisions and prognostic evaluations. However, traditional ultrasound examinations heavily depend on the operator's experience, introducing subjectivity and diagnostic inconsistencies. In recent years, deep learning technologies have demonstrated strong potential in intelligent medical imaging diagnostics, offering innovative solutions for automated and precise classification of ovarian cysts.

Results: Compared to subjective evaluations by senior ultrasound physicians (accuracy: 76.5%) and the O-RADS classification system (accuracy: 87.8%), the DenseNet121 model demonstrated a superior Area Under the Curve (AUC: 0.913 vs. 0.858, P < 0.05), indicating stronger overall discriminative ability.

Conclusions: Deep learning models based on ultrasound images can effectively address noise and feature complexity in such imaging, enabling high-precision classification of benign and malignant ovarian cysts. These models hold strong potential for clinical adoption, providing physicians with objective and reliable decision-making support.