A systematic survey of digital twins for endometriosis prediction

Abstract Endometriosis is a chronic condition of the female reproductive system that demands sustained clinical attention. It can affect the fallopian tubes, ovaries, pelvic organs, and uterine tissues, often leading to persistent pain, fertility challenges, and reduced quality of life. Although the condition is estimated to impact nearly 10% of women and girls of reproductive age worldwide, timely diagnosis remains difficult. Most patients experience a delay of 6–9 years before receiving confirmation, largely due to non-specific symptoms, limited clinical suspicion, and reliance on laparoscopy as the definitive diagnostic method. This review examines the evolution of predictive approaches in healthcare, emphasizing the importance of early identification of endometriosis. It explores how artificial intelligence and machine learning have been applied to enhance diagnostic accuracy, with models such as random forest, XGBoost, and convolutional neural networks demonstrating promising performance, achieving AUC values between 0.70 and 0.95 across recent studies. Following PRISMA-based screening, 42 studies published between 2019 and 2025 were included, focusing on prediction, machine learning, and digital-health applications. The synthesis highlights growing interest in integrating Digital Twin technology with explainable AI. Such integration has the potential to shift endometriosis care from reactive treatment to proactive, personalized prediction, enabling non-invasive early detection and more patient-specific clinical decision-making.