Abstract
Polycystic ovary syndrome affects between eight and thirteen percent of women of reproductive age and is notoriously difficult to diagnose owing to its heterogeneous presentation across ultrasound, hormonal and metabolic domains. A comprehensive 2024 review described how machine learning algorithms are now applied to ultrasound follicle counts, anthropometric measurements and biochemical panels simultaneously, allowing integration of data sources that clinicians previously had to weigh separately using rule-based criteria [1]. A 2025 systematic review catalogued dozens of models published since 2023, spanning random forest and support vector machine classifiers trained on clinical datasets to convolutional neural networks and attention-based U-Net architectures applied directly to ovarian ultrasound images, several achieving classification accuracies above ninety percent [2].
References
This work is licensed under a Creative Commons Attribution 4.0 International License.