What question did this study set out to answer?

The aim is to evaluate the diagnostic performance of deep learning models for differentiating benign from malignant ovarian cysts using ultrasound images.

April 17, 2026Open Access

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

Key Points

The aim is to evaluate the diagnostic performance of deep learning models for differentiating benign from malignant ovarian cysts using ultrasound images.
Comparison of DenseNet121 deep learning model with traditional ultrasound assessments and O-RADS classification.
Evaluation of model accuracy using Area Under the Curve (AUC) metrics.
Analysis of ultrasound images to assess cyst characteristics.
DenseNet121 achieved an AUC of 0.913, outperforming traditional methods and O-RADS classification.
Accuracy of senior ultrasound physicians was 76.5% and O-RADS was 87.8%.
The deep learning approach offers higher precision compared to subjective evaluations.

Abstract

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. 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. 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.

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