Early Detection and Risk Stratification of Uterine Fibroid using DeepLearning-A Review

Uterine fibroids represent a significant global health burden, affecting a majority of women by age
50. Current diagnostic methods, primarily ultrasound and MRI, are effective for anatomical description
but are subjective and lack predictive capability for individual risk stratification. This review synthesizes
the emerging application of deep learning (DL) in transforming the management of uterine fibroids. We
detail how convolutional neural networks (CNNs) and architectures like U-Net automate detection and
segmentation with high accuracy, outperforming traditional methods. Crucially, we explore the potential
of DL to integrate imaging radiomics with clinical and genetic data to predict future fibroid behavior,
enabling a shift from reactive to proactive, personalized care. Despite promising results, challenges
including data scarcity, algorithmic bias, and model explainability remain significant hurdles to clinical
adoption. The review concludes that overcoming these barriers through interdisciplinary collaboration
and robust validation is essential to realize the full potential of DL in improving early detection, risk
stratification, and ultimately, patient outcomes for uterine fibroids