CT-based AI system for quantitative and integrated management of acute respiratory distress syndrome in critical care

Abstract Acute respiratory distress syndrome (ARDS) remains a major challenge in critical care, with mortality exceeding 40%. Its diagnosis and management depend on multi-step procedures, invasive arterial blood gas analysis, and subjective CT interpretation, often leading to inconsistency, delayed intervention, and increased procedural burden. To address these limitations, we develop AutoARDS, an all-in-one foundation model that transforms routine chest CT into a quantitative platform, enabling integrated and reproducible assessment of diagnosis, progression, oxygenation, physiology, and prognosis within a single, non-invasive workflow, thereby supporting faster and more standardized critical-care decisions. Technically, AutoARDS proposes to employ a multi-task pretraining strategy with adversarial perturbation, distilling routine but unstructured clinical data into unified representations for fine-grained pathological learning. Trained on over 50,000 CT volumes and validated across six medical centers (6,153 individuals), AutoARDS (1) established a reproducible CT-derived biomarker linking morphological injury with disease severity, enabling standardized tracking of pulmonary progression; (2) accurately diagnosed acute respiratory failure and ARDS (AUCs = 0.97 and 0.87), facilitating early recognition and reducing diagnostic delay; (3) directly estimated the P/F ratio (PCC = 0.83), outperforming SpO 2 -based monitoring for noninvasive severity stratification and ventilation management; and (4) predicted 28-day outcomes (time-averaged AUC = 0.79), providing complementary risk assessment for clinical planning. Further analyses confirm generalizability to ARDS-associated right ventricular dysfunction (AUC = 0.76) and revealed a positive shift image-derived age residuals, reflecting disease-related imaging patterns that resemble pulmonary aging. By bridging visual information with quantitative physiology, AutoARDS exemplifies a scalable blueprint for transforming chest CT into an integrated, quantitative platform for precise and reproducible critical-care management.