Artificial intelligence and predictive analytics in obstetric anesthesia: early warning for maternal complications

Area of Science:

• Obstetric Anesthesia
• Artificial Intelligence
• Machine Learning

Background:

• Maternal morbidity and mortality are significant concerns, with current risk tools identifying only a fraction of high-risk pregnancies.
• There is a critical need for improved methods in early prediction and management of severe maternal complications.

Purpose of the Study:

• To review recent advancements in AI and ML for obstetric anesthesia.
• Focus on early prediction, decision support, and procedural guidance.
• Key areas include postpartum hemorrhage, hypertensive disease, sepsis, hemodynamic instability, neuraxial procedures, and pain management.

Main Methods:

• Synthesis of recent literature on AI and ML applications in obstetric anesthesia.
• Analysis of electronic health record (EHR)-integrated models, imaging-based ML, and multiomics data.
• Review of early warning systems, waveform analytics, and AI-assisted ultrasound.

Main Results:

• EHR-integrated and imaging-based ML models show superior performance over traditional scores for postpartum hemorrhage, placenta accreta spectrum, and pre-eclampsia.
• AI models for sepsis and epidural fever show promise but require further validation.
• AI tools can anticipate hypotension, improve neuraxial block placement, and support individualized pain management post-cesarean.

Conclusions:

• AI-enabled tools are expected to augment, not replace, clinical judgment in obstetric anesthesia.
• Successful real-world implementation hinges on external validation, equitable access, interpretable models, EHR integration, and collaboration.