Detection of Acute Respiratory Distress Syndrome Using Machine Learning

Acute Respiratory Distress Syndrome (ARDS) is a life-threatening condition characterized by rapid onset of widespread inflammation in the lungs, leading to severe respiratory failure. Early detection of ARDS is crucial for timely intervention and improved patient outcomes. This paper proposes a novel approach for ARDS detection using machine learning algorithms applied to medical imaging and clinical data. The method integrates various features extracted from chest radiographs and patient information to create a predictive model for ARDS risk assessment. Additionally, this research investigates the potential of deep learning techniques in analyzing medical images to identify specific patterns indicative of ARDS onset. The proposed system demonstrates promising results in accurately identifying and predicting the likelihood of ARDS development, providing clinicians with a valuable tool for early diagnosis and intervention. This approach holds significant promise in enhancing clinical decision-making and improving patient care in critical care settings.