Developing a Machine Learning Model Using Chest X-Ray and Blood Gas Tests to Predict Mechanical Ventilation Need in Critically Ill Adults

What this Trial Is About

This research aims to develop and validate a machine learning model that combines chest X-ray results with arterial blood gas ABG analysis to predict the need for mechanical ventilation in critically ill adult patients. The study is conducted at Zagazig University Hospitals and seeks to improve critical care decisions by integrating radiological and biochemical data using artificial intelligence, comparing the models predictions against standard clinical assessments. The study plans to enroll about 2,160 critically ill adults over six months. Participants include those clinically assessed to require mechanical ventilation and a control group of age- and sex-matched critically ill patients who do not need ventilation. Data collected include chest X-ray findings and ABG parameters such as pH, PaO2, PaCO2, and HCO3. The machine learning model will be trained on 70% of the data and tested on the remaining 30%, with performance measured by accuracy and error metrics. Participants will undergo evaluation including chest X-rays and ABG tests at the time of assessment. Researchers will collect demographic and clinical information to support model development. The study measures the models accuracy in predicting ventilation needs within 24 hours of patient presentation. Ethical approval has been obtained, and the study is designed to enhance objective and efficient management of critically ill patients using combined imaging and laboratory data.

Combining Chest X-Ray and Arterial Blood Gas Findings to Predict Need for Mechanical Ventilation in Critically Ill Patients