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

NCT07328997 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 400

Last updated 2026-01-09

No results posted yet for this study

Summary

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.

Conditions

  • ARDS (Acute Respiratory Distress Syndrome)
  • AI (Artificial Intelligence)

Interventions

DIAGNOSTIC_TEST

CT scan

CT scan

Sponsors & Collaborators

  • Shanghai Zhongshan Hospital

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
100 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-05-31
Primary Completion
2025-11-30
Completion
2025-11-30

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07328997 on ClinicalTrials.gov