Data-Driven Clinical Decision Support for Predicting Surgical Choice in Uterine Fibroid Management Using Anemia Indices and Fibroid Characteristics

Background Uterine fibroids (UFs) are common benign tumors in reproductive-age women, often requiring surgical treatment. The choice between hysterectomy and myomectomy depends on fibroid features and hematologic indices but is usually based on subjective clinical judgment. Advances in artificial intelligence (AI) and machine learning (ML) now enable data-driven decision support systems that can improve surgical accuracy, consistency, and patient outcomes. To develop and validate ML-based models capable of predicting the appropriate surgical approach—hysterectomy or myomectomy—using anemia-related laboratory parameters and uterine fibroid characteristics. Methods A retrospective multicenter study was conducted across three tertiary referral hospitals. A total of 600 women diagnosed with UFs were included, of whom 362 (60.3%) underwent hysterectomy and 238 (39.7%) underwent myomectomy. Clinical and laboratory data, including fibroid number, total fibroid volume, hemoglobin, and ferritin levels, were analyzed. Comparative statistical analyses were performed, and 126 ML models were trained and tested to predict surgical type based on these variables. A cohort of 50 cases was used for blinded real-time validation, and concordance was assessed. Results Ferritin levels, fibroid count, and total fibroid volume were significantly higher in the hysterectomy group compared with the myomectomy group (P < .001 for all). The ML models achieved accuracy rates exceeding 90% in differentiating between the two surgical approaches, effectively replicating clinicians’ decision-making behavior. Prospective validation demonstrated a high level of agreement, with 96% concordance between ML predictions and the blinded gynecologist’s assessments. Conclusions Anemia-related indices and fibroid characteristics emerged as principal factors influencing surgical decision-making in uterine fibroid management. Within an academically designed and prospectively validated framework, the ML–based model demonstrated high predictive performance and robust concordance with expert clinical assessments, supporting its methodological reliability. These findings indicate that such validated, data-driven algorithms may, following further external validation and implementation studies, be suitable for integration into gynecologic surgical workflows, where they could assist objective preoperative planning and enhance future clinical decision-making.