Clinical Research Directory
Browse clinical research sites, groups, and studies.
Development and Prospective Validation of the AutoEIT Foundation Model for ARDS
Sponsor: Ruijin Hospital
Summary
Acute Respiratory Distress Syndrome (ARDS) is a highly heterogeneous condition, making precision mechanical ventilation challenging. Electrical Impedance Tomography (EIT) provides real-time monitoring of regional lung ventilation, but interpreting its complex spatiotemporal data relies on subjective clinical experience. This multicenter ambispective cohort study aims to develop and validate an all-in-one Artificial Intelligence Foundation Model (AutoEIT). The study consists of two phases: (1) A Retrospective Phase (data from 2020 to 2025, N=1000) to train the AutoEIT model using continuous EIT images and synchronous high-frequency ventilator waveforms; and (2) A Prospective Temporal Phase (January 2026 to December 2026, N=200) to rigorously test the model's performance in a real-world clinical setting. The AutoEIT model is a deep multi-task learning framework designed to simultaneously perform five core clinical tasks: (1) classify ARDS subphenotypes, (2) recommend individualized positive end-expiratory pressure (PEEP), (3) detect occult patient-ventilator asynchrony, (4) predict ventilation/perfusion (V/Q) ratio, and (5) continuously estimate the non-invasive PaO₂/FiO₂ ratio. Ultimately, this system aims to reduce ventilator-induced lung injury (VILI) and improve precision respiratory care.
Official title: A Multimodal Artificial Intelligence Foundation Model for Precision Management of Acute Respiratory Distress Syndrome (ARDS) (AutoEIT): Development and Prospective Validation in a Multicenter Cohort
Key Details
Gender
All
Age Range
18 Years - 90 Years
Study Type
OBSERVATIONAL
Enrollment
1200
Start Date
2026-11-01
Completion Date
2027-05-31
Last Updated
2026-10-27
Healthy Volunteers
No
Conditions
Interventions
AutoEIT Model Training
Adult ARDS patients admitted to participating ICUs between January 2020 and December 2025 who received invasive mechanical ventilation and EIT monitoring. Data from this cohort will be used exclusively to train the shared spatiotemporal encoder and the five task specific prediction heads of the AutoEIT foundation model.
AutoEIT Blinded Validation
Adult ARDS patients prospectively enrolled between January 2026 and December 2026. For this cohort, strict millisecond-level synchronization between the EIT system, mechanical ventilator, and clinical events (e.g., ABG sampling) will be enforced. The AutoEIT model will run in the background (blinded to treating clinicians) to generate the 5-task predictions, which will later be compared against actual clinical ground truths.
Locations (1)
Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine
Shanghai, Shanghai Municipality, China