Exploration of Diagnosis and Treatment Strategies and Prognostic Prediction Models for Acute Respiratory Distress Syndrome Based on Radiographic Evaluations Assessed by Artificial Intelligence

December 27, 2025 updated by: Shanghai Zhongshan Hospital
By using multi-center chest CT data, an intelligent assessment model for the severity of ARDS was constructed. Based on CT quantitative features and clinical characteristics, a prediction model for short-term critical events (such as mechanical ventilation decisions, prone position strategies, death, ECMO use, etc.) was established. The disease was staged and quantified, and a diagnosis and risk stratification model for ARDS was developed to assist in guiding the diagnosis and treatment strategies for ARDS.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Actual)

400

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • China
    • Fengling Rd
      • Shanghai, Fengling Rd, China, 200032, P. R.
        • Department of critical care medicine, Zhongshan Hospital, Fudan University

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Probability Sample

Study Population

The study population consists of adult patients diagnosed with acute respiratory distress syndrome (ARDS) who were admitted to the intensive care units (ICUs) of three tertiary comprehensive hospitals in China. Eligible participants are retrospectively identified from electronic medical records between January 2020 and December 2024. All included patients meet the predefined inclusion and exclusion criteria based on the 2023 Global ARDS Definition and have available chest CT imaging and corresponding clinical data. This cohort represents a real-world ICU population with diverse etiologies of ARDS and varying degrees of disease severity.

Description

Inclusion Criteria:

  • Meets the diagnostic criteria for ARDS
  • Be admitted to the intensive care unit
  • There are chest CT images

Exclusion Criteria:

  • Age less than 18 years old
  • Missing medical records
  • No chest CT images

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Training group, testing group, validation group
The study adopts a stratified random sampling strategy with an 8:2 split to construct training and internal validation datasets, together with an independent external test cohort from a separate center. No randomization of clinical interventions or treatments is involved. The model will be developed and evaluated using observational data derived from real-world clinical pathways and outcomes, with the objectives of assessing performance in disease severity stratification, treatment recommendation, and mortality prediction. Model performance will be compared with established ICU severity scores and existing AI-based approaches according to a prespecified statistical analysis plan.
Diagnostic test: CT scan
CT scan

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Accuracy of ARDS severity classification
Time Frame: Baseline, defined as within 24 hours of index chest CT acquisition during ICU admission.
Accuracy of the artificial intelligence-based model in classifying ARDS severity (mild, moderate, or severe), using the reference clinical classification defined by the 2023 global ARDS criteria as the ground truth.
Baseline, defined as within 24 hours of index chest CT acquisition during ICU admission.
Treatment plan matching rate between model-recommended and actual clinical management.
Time Frame: Baseline, defined as within 24 hours of index chest CT acquisition during ICU admission.
Concordance rate between model-recommended treatment strategies and actual clinical management decisions across five predefined intervention modalities: mechanical ventilation, high-flow nasal oxygen therapy, non-invasive ventilation, prone positioning, and neuromuscular blockade.
Baseline, defined as within 24 hours of index chest CT acquisition during ICU admission.
Accuracy of 28-day in-hospital mortality prediction.
Time Frame: Up to 28 days from ICU admission, or until hospital discharge, whichever occurs first.
Accuracy of the model in predicting all-cause in-hospital mortality within 28 days, based on integrated chest CT imaging features and clinical variables.
Up to 28 days from ICU admission, or until hospital discharge, whichever occurs first.

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Comparative performance improvement over baseline AI models.
Time Frame: Baseline for severity classification and treatment plan matching; up to 28 days from ICU admission for mortality prediction
Absolute performance improvement of the proposed model compared with three commonly used baseline artificial intelligence models across ARDS severity classification, treatment plan matching, and 28-day mortality prediction.
Baseline for severity classification and treatment plan matching; up to 28 days from ICU admission for mortality prediction
Calibration performance of 28-day mortality prediction.
Time Frame: Up to 28 days from ICU admission, or until hospital discharge, whichever occurs first.
Calibration of the mortality prediction model assessed using calibration curves and calibration statistics to evaluate agreement between predicted and observed 28-day in-hospital mortality.
Up to 28 days from ICU admission, or until hospital discharge, whichever occurs first.
Model interpretability based on imaging and clinical feature contributions.
Time Frame: Baseline for feature extraction; up to 28 days from ICU admission for outcome association analysis.
Quantification of the relative contributions of imaging-derived features and clinical variables to mortality prediction using Shapley Additive Explanations (SHAP). Feature importance will be analyzed overall and stratified by ARDS severity.
Baseline for feature extraction; up to 28 days from ICU admission for outcome association analysis.
Association between treatment concordance and 28-day in-hospital mortality.
Time Frame: Up to 28 days from ICU admission, or until hospital discharge, whichever occurs first.
Association between concordance of model-recommended interventions and actual clinical treatments and 28-day in-hospital mortality, evaluated using multivariable logistic regression adjusted for key imaging-derived structural metrics.
Up to 28 days from ICU admission, or until hospital discharge, whichever occurs first.

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Shanghai Zhongshan Hospital

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

May 31, 2024

Primary Completion (Actual)

November 30, 2025

Study Completion (Actual)

November 30, 2025

Study Registration Dates

First Submitted

December 13, 2025

First Submitted That Met QC Criteria

December 27, 2025

First Posted (Actual)

January 9, 2026

Study Record Updates

Last Update Posted (Actual)

January 9, 2026

Last Update Submitted That Met QC Criteria

December 27, 2025

Last Verified

December 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • B2024-180

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

YES

IPD Plan Description

Individual participant data that underlie the results of the study, including de-identified demographic information, clinical variables, laboratory findings, ventilator or HFNC parameters, and AI-derived quantitative CT features, will be shared. All data will be fully de-identified in accordance with applicable regulations before sharing. Imaging data (CT scans) will also be provided in de-identified format when permitted by participating sites.

IPD Sharing Time Frame

Individual participant data will be available starting 12 months after publication of the primary results and will remain accessible for at least 5 years.

IPD Sharing Access Criteria

Individual participant data will be shared with qualified researchers for scientifically sound proposals. Requests must include a methodologically appropriate analysis plan and an institutional review board (IRB) or ethics committee approval when required. Data will be shared only in de-identified form. All requests will be reviewed by the principal investigator and the study steering committee, who will evaluate the scientific rationale, feasibility, and compliance with data-protection regulations. Upon approval, data will be accessed through a secure, password-protected data-sharing platform.

IPD Sharing Supporting Information Type

  • STUDY_PROTOCOL
  • SAP
  • ICF

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

Clinical Trials on ARDS (Acute Respiratory Distress Syndrome)

Clinical Trials on CT scan

Search Similar Trials

Sponsors and Collaborators

Medical Conditions

Drug Interventions

CROs by country

CROs in United Arab Emirates

Conditions

Rare Diseases

Drug Interventions

Dietary Supplements

Sponsor/Collaborators

Locations

Subscribe