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Deep Learning for Preoperative Pulmonary Assessment in Thoracic CT

NCT06477458 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 2000

Last updated 2024-06-27

No results posted yet for this study

Summary

The trial was designed as a single-centre, non-interventional prospective observational study to utilize deep learning technology combined with computed tomography (CT) images to precisely predict the pulmonary function indicators of thoracic surgery preoperative patients.

Conditions

  • Elective Thoracic Surgery
  • Pulmonary Function
  • Deep Learning

Interventions

OTHER

Single inspiratory phase computed tomography.

Utilizing deep learning technology in conjunction with single inspiratory phase computed tomography images to accurately predict the pulmonary function indicators of preoperative thoracic surgery patients.

OTHER

Respiratory dual-phase computed tomography.

Utilizing deep learning technology in conjunction with respiratory dual-phase computed tomography images to accurately predict the pulmonary function indicators of preoperative thoracic surgery patients.

Sponsors & Collaborators

  • GE Healthcare

    collaborator INDUSTRY

  • The First Affiliated Hospital of Guangzhou Medical University

    lead OTHER

Principal Investigators

  • Jianxing He, MD · Department of Cardiothoracic Surgery, the First Affiliated Hospital of Guangzhou Medical College

Eligibility

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

Timeline & Regulatory

Start
2023-10-01
Primary Completion
2024-09-30
Completion
2024-12-30

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 NCT06477458 on ClinicalTrials.gov