Exploration of Diagnosis and Treatment Strategies and Prognostic Prediction Models for Acute Respiratory Distress Syndrome Based on Radiographic Evaluations Assessed by Artificial Intelligence

Exploration of Diagnosis and Treatment Strategies and Prognostic Prediction Models for Acute Respiratory Distress Syndrome Based on Radiographic Evaluations Assessed by Artificial Intelligence

By using multi-center chest CT data, an intelligent assessment model for the severity of ARDS was constructed. Based on CT quantitative features and clinical characteristics, a prediction model for short-term critical events (such as mechanical ventilation decisions, prone position strategies, death, ECMO use, etc.) was established. The disease was staged and quantified, and a diagnosis and risk stratification model for ARDS was developed to assist in guiding the diagnosis and treatment strategies for ARDS.

Inclusion Criteria:

• Meets the diagnostic criteria for ARDS
• Be admitted to the intensive care unit
• There are chest CT images

Exclusion Criteria:

• Age less than 18 years old
• Missing medical records
• No chest CT images