Enterprise AI Analysis

This retrospective multicenter cohort study included 618 women with uterine fibroids, stratified by myomectomy status (238 surgical, 380 non-surgical). Data included demographics, laboratory results (HbA1c, ferritin, hormones), and fibroid characteristics. The analytical strategy involved comparative analyses, univariate and multivariate logistic regression, and machine learning modeling.

A total of 155 machine learning models were trained, with the top 20 achieving >99% accuracy. Model training used a stratified train-test split with cross-validation. Blinded concordance analysis on 50 independent cases showed 94% agreement with expert clinical judgment, supporting the model's external decision-support capability under real-world constraints. Default hyperparameters were used to prioritize interpretability over optimization, and no synthetic oversampling was applied to maintain real-world class distribution.

Integrating AI-powered decision support, as demonstrated by this study, can significantly enhance strategic planning in healthcare enterprises managing uterine fibroids. By identifying high-risk patients earlier and with greater precision, health systems can optimize surgical scheduling, reduce pre-operative complications related to anemia (linked to ferritin), and improve resource allocation.

The ability of machine learning to capture complex interactions between metabolic markers like HbA1c, iron status, and hormonal profiles provides a more holistic view for personalized patient management. This leads to better patient outcomes, reduced readmission rates, and potentially lower long-term costs associated with chronic fibroid management. Such AI frameworks serve as critical tools for standardizing care pathways and supporting evidence-based, data-driven decisions at scale.

This study's retrospective design limits causal inference and control over unmeasured confounders like symptom severity or prior treatments. HbA1c measurements were not adjusted for anemia or iron supplementation, which can influence results. Model calibration was not assessed, and blinded concordance involved a single gynecologist.

Future research should employ prospective designs with standardized assessments of anemia, iron status, insulin resistance, and fibroid symptoms. Longitudinal evaluation of HbA1c dynamics pre/post-intervention is needed. Multi-center prospective validation with calibration analyses and multiple independent clinical raters will strengthen clinical applicability. These steps are crucial for developing generalizable, robust AI-based decision-support tools for fibroid management.