Inflammatory indices, machine learning and artificial intelligence in tubal ectopic pregnancy management

Key Points

• This research aims to identify effective predictive biomarkers for managing tubal ectopic pregnancy (TEP) and evaluate machine learning applications.
• Identified inflammatory indices, including AISI, SIRI, and FAR, as predictors of treatment failure.
• Utilized machine learning models to enhance predictive accuracy.
• Proposed a personalized approach to TEP management based on these findings.
• AISI, SIRI, and FAR accurately predict methotrexate failure and the need for surgery.
• Machine learning models significantly improve prediction effectiveness.
• Results support the need for prospective validation before clinical application.