Advancing Women’s Health: Artificial Intelligence for Ovarian Disorder Detection and Classification

The ovary is a fundamental organ of the female reproductive and endocrine systems, responsible for the production of ova and the secretion of hormones that regulate fertility and overall hormonal balance. These hormones regulate the menstrual cycle, maintain hormonal balance, and support pregnancy. Disruptions in ovarian function often led to conditions such as polycystic ovary syndrome, ovarian cysts, endometriosis, and ovarian cancer. These disorders frequently progress without symptoms in early stages, making early detection essential for improving fertility outcomes and reducing clinical complications. Traditional diagnostic approaches rely on ultrasound imaging, biochemical markers such as CA125, and manual clinical evaluations. These methods are often constrained by limited sensitivity, subjectivity in interpretation, and dependency on operator expertise. Such limitations hinder timely diagnosis, particularly during the early stages of disease progression. This research systematically assessed 15 peer-reviewed studies involving ML, DL, and ensemble models, using datasets such as Kaggle PCOS, UCI clinical data, TCGA histopathology images, and hospital-based ultrasound records. The assessment compared diagnostic accuracy, precision, and reliability across approaches. ML methods such as SMOTE-based Logistic Regression achieved up to 97.11% accuracy, DL models such as VGG19 reached 96% accuracy, and ensemble frameworks offered robustness with slightly higher computational complexity. These findings highlight both the promise of AI-based approaches and the critical need for improved interpretability, multimodal integration, and clinical validation. This research focuses on AI-based models, including machine learning (ML), deep learning (DL), and ensemble techniques for detecting and classifying ovarian disorders using clinical and imaging data. The study highlights improved diagnostic performance, higher consistency, and greater efficiency in identifying complex patterns associated with ovarian pathologies. Results also reveal existing limitations in dataset diversity, lack of interpretability, and insufficient integration of clinical features across modalities. The findings support the advancement of reliable and standardized tools that enhance early detection and streamline clinical decision-making in gynaecological healthcare.