Search

The Berlin definition of ARDS provides standardized diagnostic criteria based on acute onset within one week of a known insult, bilateral chest imaging opacities not explained by other causes, respiratory failure not due to cardiac issues or fluid overload, and impaired oxygenation measured by the PaO₂/FiO₂ ratio, enabling consistent identification in intensive care; however, its clinical use is limited by variability in imaging interpretation and the need for rapid decision-making, often causing delays and inconsistent diagnoses. Current practice relies heavily on subjective assessment of chest X-rays and limited integration of clinical notes and laboratory trends, resulting in moderate inter-observer agreement and reduced diagnostic reliability. To overcome these challenges, a multimodal transformer framework is proposed that integrates chest X-rays, clinical notes, and laboratory data using vision transformers, BERT-based text encoders, and temporally aware lab embeddings, with cross-modal attention enabling interaction across data types and a fusion module producing final ARDS probability estimates. This integrated approach improves diagnostic accuracy by combining complementary information, enhances interpretability through attention mechanisms, and offers a more objective and timely method for ARDS detection, with potential to support earlier intervention and better outcomes in critically ill patients.