Abstract
Acute Respiratory Distress Syndrome (ARDS) is a life-threatening condition characterized by acute onset of respiratory failure, which presents significant challenges in diagnosis and management. Its heterogeneity, with diverse underlying aetiologies and variable patient responses to treatment, highlights the need for individualized care approaches. Accurate and timely diagnosis, coupled with personalized therapy, is essential to improving patient outcomes. In this context, the integration of advanced lung imaging techniques, such as chest computed tomography, lung ultrasound, and electrical impedance tomography, with functional parameters (like respiratory system compliance, driving pressure, transpulmonary pressure, and mechanical power) has emerged as a promising approach to enhance diagnostic precision. These imaging modalities provide valuable insights into the patient's underlying respiratory mechanics and enable better assessment of disease severity. However, the clinical application of these techniques is limited by variability in interpretation and potential interobserver discrepancies. Recent research emphasizes the need for standardized validation of these methodologies to enhance their accuracy, reliability, and predictive power. Additionally, emerging evidence suggests that mechanical ventilation strategies tailored to ARDS subphenotypes may improve outcomes for distinct patient groups. As such, the integration of imaging techniques and functional assessments promises to advance personalized management strategies for ARDS. Future research should focus on integrating lung imaging data with functional parameters, exploring innovative imaging technologies, and leveraging artificial intelligence to enhance diagnostic accuracy and optimize treatment effectiveness.