Artificial Intelligence in Ultrasound-Based Diagnoses of Gynecological Tumors: A Systematic Review

Abstract

Gynecological tumors, particularly ovarian, endometrial, and uterine masses, pose significant diagnostic challenges due to their heterogeneity and the subjective nature of ultrasound interpretation. Artificial intelligence (AI) has emerged as a promising tool to enhance diagnostic accuracy, yet its clinical adoption remains limited. This systematic review synthesizes evidence on AI applications in ultrasound-based diagnosis of gynecological tumors, evaluating performance metrics, methodological strengths, and limitations to guide future research and clinical implementation. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, a comprehensive search was conducted across PubMed, Excerpta Medica Database (Embase), Institute of Electrical and Electronics Engineers Xplore (IEEE Xplore), Scopus, and Web of Science, yielding 252 records. After removing duplicates and screening titles/abstracts, 106 studies were assessed, with 26 meeting inclusion criteria. Eligible studies investigated AI models for gynecological tumor diagnosis using ultrasound. Data were extracted on study design, sample size, AI methodology, performance metrics, and clinical applicability. Risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). Narrative synthesis was performed due to methodological heterogeneity. The 26 included studies demonstrated strong diagnostic performance, with AI models achieving accuracies of 75-99.8% and area under the curve (AUCs) up to 0.99 in differentiating benign from malignant tumors. Deep learning architectures (e.g., convolutional neural networks (CNNs), residual neural networks (ResNet)) outperformed traditional machine learning in most studies, particularly when integrating radiomics with clinical variables (e.g., cancer antigen 125 (CA-125)). However, heterogeneity in imaging protocols, sample sizes, and validation methods limited comparability. Only three studies employed prospective designs, and few addressed algorithmic bias or real-world clinical integration. AI shows significant potential to improve ultrasound-based diagnosis of gynecological tumors, offering superior accuracy and reproducibility compared to conventional methods. However, standardized imaging protocols, robust external validation, and prospective trials are needed to translate these tools into clinical practice. Future work should prioritize explainable AI, diverse datasets, and outcome studies to ensure equitable and effective implementation.

Keywords: artificial intelligence; diagnostic accuracy; gynecological tumors; machine learning; systematic review; ultrasound.

Figure 1. PRISMA Flowchart for Studies Selection Process