Project Overview
Difficult airway management is one of the most critical challenges in anesthesiology. Current assessment relies on multiple manual scoring systems: Mallampati, LEMON, thyromental distance, which are subjective and inconsistent across practitioners. This project develops a machine learning model that takes patient parameters as input and outputs a standardized difficulty prediction and risk score. The goal is to help clinicians make faster, more confident intubation decisions, reducing adverse outcomes in emergency and surgical settings. The model is being developed with a focus on clinical deployability and real world integration with pre operative assessment workflows.
Project Info
Status
In ProgressDomains
Key Highlights
Standardizes pre intubation scoring across multiple clinical assessment frameworks
Reduces subjectivity and inter practitioner inconsistency in difficult airway prediction
Designed for integration into pre operative assessment workflows
Targets reduction of adverse outcomes in emergency and surgical settings
Future Scope
Clinical validation with real patient datasets
Integration with hospital pre operative assessment systems
Expansion to cover broader anesthesiology risk assessment parameters