An AI-Driven Clinical Decision Support Model Based on Anemia and Fibroid Parameters to Guide Surgical Decision-Making

<i>Background and Objectives</i>: This study aimed to identify the clinical factors associated with the need for surgical intervention in women with uterine fibroids (UFs) and develop a data-driven clinical decision helper algorithm. By comparing hematologic and fibroid characteristics and prospectively assessing clinical concordance with the model predictions, we sought to create an objective tool for surgical decision-making. <i>Materials and Methods</i>: This retrospective study enrolled 618 women with UFs who were evaluated at three participating hospitals. Of these, 238 (38.5%) underwent surgery. Comparative statistical analyses were conducted between patients who underwent myomectomy and those who did not. Machine learning (ML) models were trained to predict myomectomy necessity. A clinical concordance assessment was conducted using 50 cases that were evaluated in real time by a gynecologist blinded to both the clinical outcomes and the model outputs. Agreement between clinical assessment and algorithm-based predictions was subsequently evaluated. <i>Results</i>: Hemoglobin and ferritin concentrations were significantly reduced in the surgery group compared with the non-surgery group (<i>p</i> < 0.001). ML analyses integrating fibroid characteristics with anemia-related markers identified support vector ML models as the most accurate classifiers. Ferritin-based models achieved accuracies of 98-99% and near-perfect ROC-AUC values. ML models combining UF number or volume with ferritin demonstrated the highest precision, sensitivity, and F1-scores. Clinical concordance analysis showed 98% agreement with the blinded gynecologist, with only one borderline discordant case. <i>Conclusions</i>: This decision helper algorithm provides a highly accurate and objective tool for predicting surgical necessity in patients with UFs. Anemia status and fibroid characteristics were the strongest predictors. By reducing subjective variability and closely reflecting expert reasoning, the model offers a practical framework for integration into routine gynecologic decision-making.